My Blog Posts, in Reverse Chronological Order
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Following Professor Michael I. Jordan's Advice: "Your Brain Needs Exercise"
The lone class I am taking this semester is STAT 210B, the second course in the PhDlevel theoretical statistics sequence. I took STAT 210A last semester, and I briefly wrote about the class here. I’ll have more to say about STAT 210B in late May, but in this post I’d first like to present an interesting problem that our professor, Michael I. Jordan, brought up in lecture a few weeks ago.
The problem Professor Jordan discussed was actually an old homework question, but he said that it was so important for us to know this that he was going to prove it in lecture anyway, without using any notes whatsoever. He also stated:
“Your brain needs exercise.”
He then went ahead and successfully proved it, and urged us to do the same thing.
OK, if he says to do that, then I will follow his advice and write out my answer in this blog post. I’m probably the only student in class who’s going to be doing this, but I’m already a bit unusual in having a longrunning blog. If any of my classmates are reading this and have their own blogs, let me know!
By the way, for all the students out there who say that they don’t have time to maintain personal blogs, why not take baby steps and start writing about stuff that accomplishes your educational objectives, such as doing practice exercises? It’s a nice way to make yourself look more productive than you actually are, since you would be doing those anyway.
Anyway, here at last is the question Professor Jordan talked about:
Let \(\{X_i\}_{i=1}^n\) be a sequence of zeromean random variables, each subGaussian with parameter \(\sigma\) (No independence assumptions are needed). Prove that
\[\mathbb{E}\Big[\max_{i=1,\ldots,n}X_i\Big] \le \sqrt{2\sigma^2 \log n}\]for all \(n\ge 1\).
This problem is certainly on the easier side of the homework questions we’ve had, but it’s a good baseline and I’d like to showcase the solution here. Like Professor Jordan, I will do this problem (a.k.a. write this blog post) without any form of notes. Here goes: for \(\lambda \ge 0\), we have
\[\begin{align} e^{\lambda \mathbb{E}[\max\{X_1, \ldots, X_n\}]} \;&{\overset{(i)}{\le}}\;\mathbb{E}[e^{\lambda \max\{X_1,\ldots,X_n\}}] \\ \;&{\overset{(ii)}{=}}\; \mathbb{E}[\max\{e^{\lambda X_1},\ldots,e^{\lambda X_n}\}] \\ \;&{\overset{(iii)}{\le}}\; \sum_{i=1}^n\mathbb{E}[e^{\lambda X_i}] \\ \;&{\overset{(iv)}{\le}}\; ne^{\frac{\lambda^2\sigma^2}{2}} \end{align}\]where:
 Step (i) follows from Jensen’s inequality. Yeah, that inequality is everywhere.
 Step (ii) follows from noting that one can pull the maximum outside of the exponential.
 Step (iii) follows from the classic union bound, which can be pretty bad but we don’t have much else to go on here. The key fact is that the exponential makes all terms in the sum positive.
 Step (iv) follows from applying the subGaussian bound to all \(n\) variables, and then summing them together.
Next, taking logs and rearranging, we have
\[\mathbb{E}\Big[\max\{X_1, \ldots, X_n\}\Big] \le \frac{\log n}{\lambda} + \frac{\lambda\sigma^2}{2}\]Since \(\lambda \in \mathbb{R}\) is isolated on the right hand side, we can differentiate it to find the tightest lower bound. Doing so, we get \(\lambda^* = \frac{\sqrt{2 \log n}}{\sigma}\). Plugging this back in, we get
\[\begin{align} \mathbb{E}\Big[\max\{X_1, \ldots, X_n\}\Big] &\le \frac{\log n}{\lambda} + \frac{\lambda\sigma^2}{2} \\ &\le \frac{\sigma \log n}{\sqrt{2 \log n}} + \frac{\sigma^2\sqrt{2 \log n}}{2 \sigma} \\ &\le \frac{\sqrt{2 \sigma^2 \log n}}{2} + \frac{\sqrt{2 \sigma^2 \log n}}{2} \\ \end{align}\]which proves the desired claim.
I have to reiterate that this problem is easier than the others we’ve done in STAT 210B, and I’m sure that over 90 percent of the students in the class could do this just as easily as I could. But this problem makes clear the techniques that are often used in theoretical statistics nowadays, so at minimum students should have a firm grasp of the content in this blog post.
Update April 23, 2017: In an earlier version of this post, I made an error with taking a maximum outside of an expectation. I have fixed this post. Thanks to Billy Fang for letting me know about this.
What I Wish People Would Say About Diversity
The two mainstream newspapers that I read the most, The New York Times and The Wall Street Journal, both have recent articles about diversity and the tech industry, a topic which by now has considerable and welldeserved attention.
The New York Times article starts out with:
Like other Silicon Valley giants, Facebook has faced criticism over whether its work force and board are too white and too male. Last year, the social media behemoth started a new push on diversity in hiring and retention.
Now, it is extending its efforts into another corner: the outside lawyers who represent the company in legal matters.
Facebook is requiring that women and ethnic minorities account for at least 33 percent of law firm teams working on its matters.
The Wall Street Journal article says:
The tech industry has been under fire for years over the large percentage of white and Asian male employees and executives. Tech firms have started initiatives to try to combat the trend, but few have shown much progress.
The industry is now under scrutiny from the Labor Department for the issue. The department sued software giant Oracle Corp. earlier this year for allegedly paying white male workers more than other employees. Oracle said at the time of the suit that the complaint was politically motivated, based on false allegations, and without merit.
These articles discuss important issues that need to be addressed in the tech industry. However, I would also like to gently bring up some other points that I think should be considered in tandem.

The first is to clearly identify Asians (and multiracials^{1}) as either belonging to a minority group or not. To its credit, the Wall Street Journal article states this when including Asians among the “large percentage of employees”, but I often see this fact elided in favor of just “white males.” This is a broader issue which also arises when debating about affirmative action. Out of curiosity, I opened up the Supreme Court’s opinions on Fisher v. University of Texas at Austin (PDF link) and did a search for the word “Asians”, which appears 66 times. Only four of those instances appear in the majority opinion written by Justice Kennedy supporting raceconscious admission; the other 62 occurrences of “Asians” are in in Justice Alito’s dissent.

The second is to suggest that there are people who have good reason to believe that they would substantially contribute to workplace diversity, or who have had to overcome considerable life challenges (which I argue also increases work diversity), but who might otherwise not be considered a minority. For instance, suppose a recent refugee from Syria with some computer programming background applied to work at Google. If I were managing a hiring committee and I knew of the applicant’s background information, I would be inspired and would hold him to a slightly lower standard as other applicants, even if he happened to be white and male. There are other possibilities, and one could argue that poor whites or people who are disabled should qualify.

The third is to identify that there is a related problem in the tech industry about the pool of qualified employees to begin with. If the qualified applicants to tech jobs follow a certain distribution of the overall population, then the most likely outcome is that the people who get hired mirror that distribution. Thus, I would encourage emphasis on rephrasing the argument as follows: “tech companies have been under scrutiny for having a workforce which consists of too many white and Asian males with respect to the population distribution of qualified applicants” (emphasis mine). The words “qualified applicants” might be loaded, though. Tech companies often filter students based on school because that is an easy and accurate way to identify the top students, and in some schools (such as the one I attend, for instance), the proportion of underrepresented minorities as traditionally defined has remained stagnant for decades.
I don’t want to sound insensitive to the need to make the tech workforce more diverse. Indeed, that’s the opposite of what I feel, and I think (though I can’t say for sure) that I would be more sensitive to the needs of underrepresented minorities given my frequent experience of feeling like an outcast among my classmates and colleagues.^{2} I just hope that my alternative perspective is compatible with increasing diversity and can work alongside — rather than against — the prevailing view.

See my earlier blog post about this. ↩

I also take offense at the stereotype of the computer scientist as a “shy, nerdy, antisocial male” and hope that it gets eradicated. I invite the people espousing this stereotype to live in my shoes for a day. ↩
Sir Tim BernersLee Wins the Turing Award
The news is out that Sir Tim BernersLee has won the 2016 Turing Award, the highest honor in computer science. (Turing Award winners are usually announced a few months after the actual year of the award.) He is best known for inventing the World Wide Web, as clearly highlighted by the ACM’s citation:
For inventing the World Wide Web, the first web browser, and the fundamental protocols and algorithms allowing the Web to scale.
(You can also find more information about some of his work on his personal website, where he has some helpful FAQs.)
My first reaction to reading the news was: he didn’t already have a Turing Award?!? I actually thought he had been a cowinner with Vinton Cerf and Robert Kahn, but nope. At least he’s won it now, so we won’t be asking Quora posts like this one anymore.
I’m rather surprised that this announcement wasn’t covered by many mainstream newspapers. I tried searching for something in the New York Times, but nothing showed up. This is rather a shame, because if we think of inventing the World Wide Web as the “bar” for the Turing Award, then that’s a pretty high bar.
My prediction for the winner was actually Geoffrey Hinton, but I can’t argue with Sir Tim BernersLee. (Thus, Hinton is going to be my prediction for the 2017 award.) Just like Terrence Tao for the Fields Medalist, Steven Weinberg for the Nobel Prize in Physics, Merrick Garland for the Supreme Court, and so on, they’re so utterly qualified that I can’t think of a reason to oppose them.
Notes on the Generalized Advantage Estimation Paper
This post serves as a continuation of my last post on the fundamentals of policy gradients. Here, I continue it by discussing the Generalized Advantage Estimation (arXiv link) paper from ICLR 2016, which presents and analyzes more sophisticated forms of policy gradient methods.
Recall that raw policy gradients, while unbiased, have high variance. This paper proposes ways to dramatically reduce variance, but this unfortunately comes at the cost of introducing bias, so one needs to be careful before applying tricks like this in practice.
The setting is the usual one which I presented in my last post, and we are indeed trying to maximize the sum of rewards (assume no discount). I’m happy that the paper includes a concise set of notes summarizing policy gradients:
If the above is not 100% clear to you, I recommend reviewing the basics of policy gradients. I covered five of the six forms of the \(\Psi_t\) function in my last post; the exception is the temporal difference residual, but I will go over these later here.
Somewhat annoyingly, they use the infinitehorizon setting. I find it easier to think about the finite horizon case, and I will clarify if I’m assuming that.
Proposition 1: \(\gamma\)Just Estimators.
One of the first things they prove is Proposition 1, regarding “\(\gamma\)just” advantage estimators. (The word “just” seems like an odd choice here, but I’m not complaining.) Suppose \(\hat{A}_t(s_{0:\infty},a_{0:\infty})\) is an estimate of the advantage function. A \(\gamma\)just estimator (of the advantage function) results in
\[\mathbb{E}_{s_{0:\infty},a_{0:\infty}}\left[\hat{A}_t(s_{0:\infty},a_{0:\infty}) \nabla_\theta \log \pi_{\theta}(a_ts_t)\right]= \mathbb{E}_{s_{0:\infty},a_{0:\infty}}\left[A^{\pi,\gamma}(s_{0:\infty},a_{0:\infty}) \nabla_\theta \log \pi_{\theta}(a_ts_t)\right]\]This is for one time step \(t\). If we sum over all time steps, by linearity of expectation we get
\[\mathbb{E}_{s_{0:\infty},a_{0:\infty}}\left[\sum_{t=0}^\infty \hat{A}_t(s_{0:\infty},a_{0:\infty}) \nabla_\theta \log \pi_{\theta}(a_ts_t)\right]= \mathbb{E}_{s_{0:\infty},a_{0:\infty}}\left[\sum_{t=0}^\infty A^{\pi,\gamma}(s_t,a_t)\nabla_\theta \log \pi_{\theta}(a_ts_t)\right]\]In other words, we get an unbiased estimate of the discounted gradient. Note, however, that this discounted gradient is different from the gradient of the actual function we’re trying to optimize, since that was for the undiscounted rewards. The authors emphasize this in a footnote, saying that they’ve already introduced bias by even assuming the use of a discount factor. (I’m somewhat pleased at myself for catching this in advance.)
The proof for Proposition 1 is based on proving it for one time step \(t\), which is all that is needed. The resulting term with \(\hat{A}_t\) in it splits into two terms due to linearity of expectation, one with the \(Q_t\) function and another with the baseline. The second term is zero due to the baseline causing the expectation to zero, which I derived in my previous post in the finitehorizon case. (I’m not totally sure how to do this in the infinite horizon case, due to technicalities involving infinity.)
The first term is unfortunately a little more complicated. Let me use the finite horizon \(T\) for simplicity so that I can easily write out the definition. They argue in the proof that:
\[\begin{align} &\mathbb{E}_{s_{0:T},a_{0:T}}\left[ \nabla_\theta \log \pi_{\theta}(a_ts_t) \cdot Q_t(s_{0:T},a_{0:T})\right] \\ &= \mathbb{E}_{s_{0:t},a_{0:t}}\left[ \nabla_\theta \log \pi_{\theta}(a_ts_t)\cdot \mathbb{E}_{s_{t+1:T},a_{t+1:T}}\Big[Q_t(s_{0:T},a_{0:T})\Big]\right] \\ &= \int_{s_0}\cdots \int_{s_t}\int_{a_t}\Bigg[ p_\theta((s_0,\ldots,s_t,a_t)) \nabla_\theta \log \pi_{\theta}(a_ts_t) \cdot \mathbb{E}_{s_{t+1:T},a_{t+1:T}}\Big[ Q_t(s_{0:T},a_{0:T}) \Big]\Bigg] d\mu(s_0,\ldots,s_t,a_t)\\ \;&{\overset{(i)}{=}}\; \int_{s_0}\cdots \int_{s_t} \left[ p_\theta((s_0,\ldots,s_t)) \nabla_\theta \log \pi_{\theta}(a_ts_t) \cdot A^{\pi,\gamma}(s_t,a_t)\right] d\mu(s_0,\ldots,s_t) \end{align}\]Most of this proceeds by definitions of expectations and then “pushing” integrals into their appropriate locations. Unfortunately, I am unable to figure out how they did step (i). Specifically, I don’t see how the integral over \(a_t\) somehow “moves past” the \(\nabla_\theta \log \pi_\theta(a_ts_t)\) term. Perhaps there is some trickery with the law of iterated expectation due to conditionals? If anyone else knows why and is willing to explain with detailed math somewhere, I would really appreciate it.
For now, I will assume this proposition to be true. It is useful because if we are given the form of estimator \(\hat{A}_t\) of the advantage, we can immediately tell if it is an unbiased advantage estimator.
Advantage Function Estimators
Now assume we have some function \(V\) which attempts to approximate the true value function \(V^\pi\) (or \(V^{\pi,\gamma}\) in the undiscounted setting).

Note I: \(V\) is not the true value function. It is only our estimate of it, so \(V_\phi(s_t) \approx V^\pi(s_t)\). I added in the \(\phi\) subscript to indicate that we use a function, such as a neural network, to approximate the value. The weights of the neural network are entirely specified by \(\phi\).

Note II: we also have our policy \(\pi_\theta\) parameterized by parameters \(\theta\), again typically a neural network. For now, assume that \(\phi\) and \(\theta\) are separate parameters; the authors mention some enticing future work where one can share parameters and jointly optimize. The combination of \(\pi_{\theta}\) and \(V_{\phi}\) with a policy estimator and a value function estimator is known as the actorcritic model with the policy as the actor and the value function as the critic. (I don’t know why it’s called a “critic” because the value function acts more like an “assistant”.)
Using \(V\), we can derive a class of advantage function estimators as follows:
\[\begin{align} \hat{A}_t^{(1)} &= r_t + \gamma V(s_{t+1})  V(s_t) \\ \hat{A}_t^{(2)} &= r_t + \gamma r_{t+1} + \gamma^2 V(s_{t+2})  V(s_t) \\ \cdots &= \cdots \\ \hat{A}_t^{(\infty)} &= r_t + \gamma r_{t+1} + \gamma^2 r_{t+2} + \cdots  V(s_t) \end{align}\]These take on the form of temporal difference estimators where we first estimate the sum of discounted rewards and then we subtract the value function estimate of it. If \(V = V^{\pi,\gamma}\), meaning that \(V\) is exact, then all of the above are unbiased estimates for the advantage function. In practice, this will not be the case, since we are not given the value function.
The tradeoff here is that the estimators \(\hat{A}_t^{(k)}\) with small \(k\) have low variance but high bias, whereas those with large \(k\) have low bias but high variance. Why? I think of it based on the number of terms. With small \(k\), we have fewer terms to sum over (which means low variance). However, the bias is relatively large because it does not make use of extra “exact” information with \(r_K\) for \(K > k\). Here’s another way to think of it as emphasized in the paper: \(V(s_t)\) is constant among the estimator class, so it does not affect the relative bias or variance among the estimators: differences arise entirely due to the \(k\)step returns.
One might wonder, as I originally did, how to make use of the \(k\)step returns in practice. In Qlearning, we have to update the parameters (or the \(Q(s,a)\) “table”) after each current reward, right? The key is to let the agent run for \(k\) steps, and then update the parameters based on the returns. The reason why we update parameters “immediately” in ordinary Qlearning is simply due to the definition of Qlearning. With longer returns, we have to keep the Qvalues fixed until the agent has explored more. This is also emphasized in the A3C paper from DeepMind, where they talk about \(n\)step Qlearning.
The Generalized Advantage Estimator
It might not be so clear which of these estimators above is the most useful. How can we compute the bias and variance?
It turns out that it’s better to use all of the estimators, in a clever way. First, define the temporal difference residual \(\delta_t^V = r_t + \gamma V(s_{t+1})  V(s_t)\). Now, here’s how the Generalized Advantage Estimator \(\hat{A}_t^{GAE(\gamma,\lambda)}\) is defined:
\[\begin{align} \hat{A}_t^{GAE(\gamma,\lambda)} &= (1\lambda)\Big(\hat{A}_{t}^{(1)} + \lambda \hat{A}_{t}^{(2)} + \lambda^2 \hat{A}_{t}^{(3)} + \cdots \Big) \\ &= (1\lambda)\Big(\delta_t^V + \lambda(\delta_t^V + \gamma \delta_{t+1}^V) + \lambda^2(\delta_t^V + \gamma \delta_{t+1}^V + \gamma^2 \delta_{t+2}^V)+ \cdots \Big) \\ &= (1\lambda)\Big( \delta_t^V(1+\lambda+\lambda^2+\cdots) + \gamma\delta_{t+1}^V(\lambda+\lambda^2+\cdots) + \cdots \Big) \\ &= (1\lambda)\left(\delta_t^V \frac{1}{1\lambda} + \gamma \delta_{t+1}^V\frac{\lambda}{1\lambda} + \cdots\right) \\ &= \sum_{l=0}^\infty (\gamma \lambda)^l \delta_{t+l}^{V} \end{align}\]To derive this, one simply expands the definitions and uses the geometric series formula. The result is interesting to interpret: the exponentiallydecayed sum of residual terms.
The above describes the estimator \(GAE(\gamma, \lambda)\) for \(\lambda \in [0,1]\) where adjusting \(\lambda\) adjusts the biasvariance tradeoff. We usually have \({\rm Var}(GAE(\gamma, 1)) > {\rm Var}(GAE(\gamma, 0))\) due to the number of terms in the summation (more terms usually means higher variance), but the bias relationship is reversed. The other parameter, \(\gamma\), also adjusts the biasvariance tradeoff … but for the GAE analysis it seems like the \(\lambda\) part is more important. Admittedly, it’s a bit confusing why we need to have both \(\gamma\) and \(\lambda\) (after all, we can absorb them into one constant, right?) but as you can see, the constants serve different roles in the GAE formula.
To make a long story short, we can put the GAE in the policy gradient estimate and we’ve got our biased estimate (unless \(\lambda=1\)) of the discounted gradient, which again, is itself biased due to the discount. Will this work well in practice? Stay tuned …
Reward Shaping Interpretation
Reward shaping originated from a 1999 ICML paper, and refers to the technique of transforming the original reward function \(r\) into a new one \(\tilde{r}\) via the following transformation with \(\Phi: \mathcal{S} \to \mathbb{R}\) an arbitrary realvalued function on the state space:
\[\tilde{r}(s,a,s') = r(s,a,s') + \gamma \Phi(s')  \Phi(s)\]Amazingly, it was shown that despite how \(\Phi\) is arbitrary, the reward shaping transformation results in the same optimal policy and optimal policy gradient, at least when the objective is to maximize discounted rewards \(\sum_{t=0}^\infty \gamma^t r(s_t,a_t,s_{t+1})\). I am not sure whether the same is true with the undiscounted case as they have here, but it seems like it should since we can set \(\gamma=1\).
The more important benefit for their purposes, it seems, is that this reward shaping leaves the advantage function invariant for any policy. The word “invariant” here means that if we computed the advantage function \(A^{\pi,\gamma}\) for a policy and a discount factor in some MDP, the transformed MDP would have some advantage function \(\tilde{A}^{\pi,\gamma}\), but we would have \(A^{\pi,\gamma} = \tilde{A}^{\pi,\gamma}\) (nice!). This follows because if we consider the discounted sum of rewards starting at state \(s_t\) in the transformed MDP, we get
\[\begin{align} \sum_{l=0}^{\infty} \gamma^l \tilde{r}(s_{t+l},a_{t+l},s_{t+l+1}) &= \left[\sum_{l=0}^{\infty}\gamma^l r(s_{t+l},a_{t+l},s_{t+l+1})\right] + \Big( \gamma\Phi(s_{t+1})  \Phi(s_t) + \gamma^2\Phi(s_{t+2})\gamma \Phi(s_{t+1})+ \cdots\Big)\\ &= \sum_{l=0}^{\infty}\gamma^l r(s_{t+l},a_{t+l},s_{t+l+1})  \Phi(s_t) \end{align}\]“Hitting” the above values with expectations (as Michael I. Jordan would say it) and substituting appropriate values results in the desired \(\tilde{A}^{\pi,\gamma}(s_t,a_t) = A^{\pi,\gamma}(s_t,a_t)\) equality.
The connection between reward shaping and the GAE is the following: suppose we are trying to find a good policy gradient estimate for the transformed MDP. If we try to maximize the sum of \((\gamma \lambda)\)discounted sum of (transformed) rewards and set \(\Phi = V\), we get precisely the GAE! With \(V\) here, we have \(\tilde{r}(s_t,a_t,s_{t+1}) = \delta_t^V\), the residual term defined earlier.
To analyze the tradeoffs with \(\gamma\) and \(\lambda\), they use a response function:
\[\chi(l; s_t,a_t) := \mathbb{E}[r_{l+t} \mid s_t,a_t]  \mathbb{E}[r_{l+t} \mid s_t]\]Why is this important? They state it clearly:
The response function lets us quantify the temporal credit assignment problem: long range dependencies between actions and rewards correspond to nonzero values of the response function for \(l \gg 0\).
These “longrange dependencies” are the most challenging part of the credit assignment problem. Then here’s the kicker: they argue that if \(\Phi = V^{\pi,\gamma}\), then the transformed rewards are such that \(\mathbb{E}[\tilde{r}_{l+t} \mid s_t,a_t]  \mathbb{E}[\tilde{r}_{l+t} \mid s_t] = 0\) for \(l>0\). Thus, longrange rewards have to induce an immediate response! I’m admittedly not totally sure if I understand this, and it seems odd that we only want the response function to be nonzero at the current time (I mean, some rewards have to be merely a few steps in the future, right?). I will take another look at this section if I have time.
Value Function Estimation
In order to be able to use the GAE in our policy gradient algorithm (again, this means computing gradients and shifting the weights of the policy to maximize an objective), we need some value function \(V_\phi\) parameterized by a neural network. This is part of the actorcritic framework, where the “critic” provides the value function estimate.
Let \(\hat{V}_t = \sum_{l=0}^\infty \gamma^l r_{t+l}\) be the discounted sum of rewards. The authors propose the following optimization procedure to find the best weights \(\phi\):
\[{\rm minimize}_\phi \quad \sum_{n=1}^N\V_\phi(s_n)  \hat{V}_n\_2^2\] \[\mbox{subject to} \quad \frac{1}{N}\sum_{n=1}^N\frac{\V_\phi(s_n)  \hat{V}_{\phi_{\rm old}}(s_n)\_2^2}{2\sigma^2} \le \epsilon\]where each iteration, \(\phi_{\rm old}\) is the parameter vector before the update, and
\[\sigma^2 = \frac{1}{N}\sum_{n=1}^N\V_{\phi_{\rm old}}(s_n)\hat{V}_n\_2^2\]This is a constrained optimization problem to find the best weights for the value function. The constraint reminds me of Trust Region Policy Optimization, because it limits the amount that \(\phi\) can change from one update to another. The advantages with a “trust region” method are that the weights don’t change too much and that they don’t overfit to the current batch. (Updates are done in batch mode, which is standard nowadays.)

Note I: unfortunately, the authors don’t use this optimization procedure exactly. They use a conjugate gradient method to approximate it. But think of the optimization procedure here since it’s easier to understand and is “ideal.”

Note II: remember that this is not the update to the policy \(\pi_\theta\). That update requires an entirely separate optimization procedure. Don’t get confused between the two. Both the policy and the value functions can be implemented as neural networks, and in fact, that’s what the authors do. They actually have the same architecture, with the exception of the output layer since the value only needs a scalar, whereas the policy needs a higherdimensional output vector.
Putting it All Together
It’s nice to understand each of the components above, but how do we combine them into an actual algorithm? Here’s a rough description of their proposed actorcritic algorithm, each iteration:

Simulate the current policy to collect data.

Compute the Bellman residuals \(\delta_{t}^V\).

Compute the advantage function estimate \(\hat{A}_t\).

Update the policy’s weights, \(\theta_{i+1}\), with a TRPO update.

Update the critic’s weights, \(\phi_{i+1}\), with a trustregion update.
As usual, here are a few of my overlydetailed comments (sorry again):

Note I: Yes, there are trust region methods for both the value function update and the policy function update. This is one of their contributions. (To be clear, the notion of a “GAE” isn’t entirely their contribution.) The value and policy are also both neural networks with the same architecture except for the output since they have different outputs. Honestly, it seems like we should always be thinking about trust region methods whenever we have some optimization to do.

Note II: If you’re confused by the role of the two networks, repeat this to yourself: the policy network is for determining actions, and the value network is for improving the performance of the gradient update (which is used to improve the actual policy by pointing the gradient in the correct direction!).
They present some impressive experimental benchmarks using this actorcritic algorithm. I don’t have too much experience with MuJoCo so I can’t intuitively think about the results that much. (I’m also surprised that MuJoCo isn’t free and requires payment; it must be by far the best physics simulator for reinforcement learning, otherwise people wouldn’t be using it.)
Concluding Thoughts
I didn’t understand the implications of this paper when I read it for the first time (maybe more than a year ago!) but it’s becoming clearer now. They present and analyze a specific kind of estimator, the GAE, which has a biasvariance “knob” with the \(\lambda\) (and \(\gamma\), technically). By adjusting the knob, it might be possible to get low variance, low biased estimates, which would drastically improve the sample efficiency of policy gradient methods. They also present a way to estimate the value method using a trust region method. With these components, they are able to achieve high performance on challenging reinforcement learning tasks with continuous control.
Going Deeper Into Reinforcement Learning: Fundamentals of Policy Gradients
As I stated in my last blog post, I am feverishly trying to read more research papers. One category of papers that seems to be coming up a lot recently are those about policy gradients, which are a popular class of reinforcement learning algorithms which estimate a gradient for a function approximator. Thus, the purpose of this blog post is for me to explicitly write the mathematical foundations for policy gradients so that I can gain understanding. In turn, I hope some of my explanations will be useful to a broader audience of AI students.
Assumptions and Problem Statement
In any type of research domain, we always have to make some set of assumptions. (By “we”, I refer to the researchers who write papers on this.) With reinforcement learning and policy gradients, the assumptions usually mean the episodic setting where an agent engages in multiple trajectories in its environment. As an example, an agent could be playing a game of Pong, so one episode or trajectory consists of a full starttofinish game.
We define a trajectory \(\tau\) of length \(T\) as
\[\tau = (s_0, a_0, r_0, s_1, a_1, r_1, \ldots, s_{T1}, a_{T1}, r_{T1}, s_T)\]where \(s_0\) comes from the starting distribution of states, \(a_i \sim \pi_\theta(a_i s_i)\), and \(s_i \sim P(s_i  s_{i1},a_{i1})\) with \(P\) the dynamics model (i.e. how the environment changes). We actually ignore the dynamics when optimizing, since all we care about is getting a good gradient signal for \(\pi_\theta\) to make it better. If this isn’t clear now, it will be clear soon. Also, the reward can be computed from the states and actions, since it’s usually a function of \((s_i,a_i,s_{i+1})\), so it’s not technically needed in the trajectory.
What’s our goal here with policy gradients? Unlike algorithms such as DQN, which strive to find an excellent policy indirectly through Qvalues, policy gradients perform a direct gradient update on a policy to change its parameters, which is what makes it so appealing. Formally, we have:
\[{\rm maximize}_{\theta}\; \mathbb{E}_{\pi_{\theta}}\left[\sum_{t=0}^{T1}\gamma^t r_t\right]\]
Note I: I put \(\pi_{\theta}\) under the expectation. This means the rewards are computed from a trajectory which was generated under the policy \(\pi_\theta\). We have to find “optimal” settings of \(\theta\) to make this work.

Note II: we don’t need to optimize the expected sum of discounted rewards, though it’s the formulation I’m most used to. Alternatives include ignoring \(\gamma\) by setting it to one, extending \(T\) to infinity if the episodes are infinitehorizon, and so on.
The above raises the allimportant question: how do we find the best \(\theta\)? If you’ve taken optimization classes before, you should know the answer already: perform gradient ascent on \(\theta\), so we have \(\theta \leftarrow \theta + \alpha \nabla f(x)\) where \(f(x)\) is the function being optimized. Here, that’s the expected value of whatever sum of rewards formula we’re using.
Two Steps: LogDerivative Trick and Determining Log Probability
Before getting to the computation of the gradient, let’s first review two mathematical facts which will be used later, and which are also of independent interest. The first is the “logderivative” trick, which tells us how to insert a log into an expectation when starting from \(\nabla_\theta \mathbb{E}[f(x)]\). Specifically, we have:
\[\begin{align} \nabla_\theta \mathbb{E}[f(x)] &= \nabla_\theta \int p_\theta(x)f(x)dx \\ &= \int \frac{p_\theta(x)}{p_\theta(x)} \nabla_\theta p_\theta(x)f(x)dx \\ &= \int p_\theta(x)\nabla_\theta \log p_\theta(x)f(x)dx \\ &= \mathbb{E}\Big[f(x)\nabla_\theta \log p_\theta(x)\Big] \end{align}\]where \(p_\theta\) is the density of \(x\). Most of these steps should be straightforward. The main technical detail to worry about is exchanging the gradient with the integral. I have never been comfortable in knowing when we are allowed to do this or not, but since everyone else does this, I will follow them.
Another technical detail we will need is the gradient of the log probability of a trajectory since we will later switch \(x\) from above with a trajectory \(\tau\). The computation of \(\log p_\theta(\tau)\) proceeds as follows:
\[\begin{align} \nabla_\theta \log p_\theta(\tau) &= \nabla \log \left(\mu(s_0) \prod_{t=0}^{T1} \pi_\theta(a_ts_t)P(s_{t+1}s_t,a_t)\right) \\ &= \nabla_\theta \left[\log \mu(s_0)+ \sum_{t=0}^{T1} (\log \pi_\theta(a_ts_t) + \log P(s_{t+1}s_t,a_t)) \right]\\ &= \nabla_\theta \sum_{t=0}^{T1}\log \pi_\theta(a_ts_t) \end{align}\]The probability of \(\tau\) decomposes into a chain of probabilities by the Markov Decision Process assumption, whereby the next action only depends on the current state, and the next state only depends on the current state and action. To be explicit, we use the functions that we already defined: \(\pi_\theta\) and \(P\) for the policy and dynamics, respectively. (Here, \(\mu\) represents the starting state distribution.) We also observe that when taking gradients, the dynamics disappear!
Computing the Raw Gradient
Using the two tools above, we can now get back to our original goal, which was to compute the gradient of the expected sum of (discounted) rewards. Formally, let \(R(\tau)\) be the reward function we want to optimize (i.e. maximize). Using the above two tricks, we obtain:
\[\nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}[R(\tau)] = \mathbb{E}_{\tau \sim \pi_\theta} \left[R(\tau) \cdot \nabla_\theta \left(\sum_{t=0}^{T1}\log \pi_\theta(a_ts_t)\right)\right]\]In the above, the expectation is with respect to the policy function, so think of it as \(\tau \sim \pi_\theta\). In practice, we need trajectories to get an empirical expectation, which estimates this actual expectation.
So that’s the gradient! Unfortunately, we’re not quite done yet. The naive way is to run the agent on a batch of episodes, get a set of trajectories (call it \(\hat{\tau}\)) and update with \(\theta \leftarrow \theta + \alpha \nabla_\theta \mathbb{E}_{\tau \in \hat{\tau}}[R(\tau)]\) using the empirical expectation, but this will be too slow and unreliable due to high variance on the gradient estimates. After one batch, we may exhibit a wide range of results: much better performance, equal performance, or worse performance. The high variance of these gradient estimates is precisely why there has been so much effort devoted to variance reduction techniques. (I should also add from personal research experience that variance reduction is certainly not limited to reinforcement learning; it also appears in many statistical projects which concern a biasvariance tradeoff.)
How to Introduce a Baseline
The standard way to reduce the variance of the above gradient estimates is to insert a baseline function \(b(s_t)\) inside the expectation.
For concreteness, assume \(R(\tau) = \sum_{t=0}^{T1}r_t\), so we have no discounted rewards. We can express the policy gradient in three equivalent, but perhaps nonintuitive ways:
\[\begin{align} \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}\Big[R(\tau)\Big] \;&{\overset{(i)}{=}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[\left(\sum_{t=0}^{T1}r_t\right) \cdot \nabla_\theta \left(\sum_{t=0}^{T1}\log \pi_\theta(a_ts_t)\right)\right] \\ &{\overset{(ii)}{=}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t'=0}^{T1} r_{t'} \sum_{t=0}^{t'}\nabla_\theta \log \pi_\theta(a_ts_t)\right] \\ &{\overset{(iii)}{=}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}r_{t'}\right) \right] \end{align}\]Comments:

Step (i) follows from plugging in our chosen \(R(\tau)\) into the policy gradient we previously derived.

Step (ii) follows from first noting that \(\nabla_\theta \mathbb{E}_{\tau}\Big[r_{t'}\Big] = \mathbb{E}_\tau\left[r_{t'} \cdot \sum_{t=0}^{t'} \nabla_\theta \log \pi_\theta(a_ts_t)\right]\). The reason why this is true can be somewhat tricky to identify. I find it easy to think of just redefining \(R(\tau)\) as \(r_{t'}\) for some fixed timestep \(t'\). Then, we do the exact same computation above to get the final result, as shown in the equation of the “Computing the Raw Gradient” section. The main difference now is that since we’re considering the reward at time \(t'\), our trajectory under expectation stops at that time. More concretely, \(\nabla_\theta\mathbb{E}_{(s_0,a_0,\ldots,s_{T})}\Big[r_{t'}\Big] = \nabla_\theta\mathbb{E}_{(s_0,a_0,\ldots,s_{t'})}\Big[r_{t'}\Big]\). This is like “throwing away variables” when taking expectations due to “pushing values” through sums and summing over densities (which cancel out); I have another example later in this post which makes this explicit.
Next, we sum over both sides, for \(t' = 0,1,\ldots,T1\). Assuming we can exchange the sum with the gradient, we get
\[\begin{align} \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta} \left[ R(\tau) \right] &= \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t'=0}^{T1} r_{t'}\right] \\ &= \sum_{t'=0}^{T1}\nabla_\theta \mathbb{E}_{\tau^{(t')}} \Big[r_{t'}\Big] \\ &= \sum_{t'}^{T1} \mathbb{E}_{\tau^{(t')}}\left[r_{t'} \cdot \sum_{t=0}^{t'} \nabla_\theta \log \pi_\theta(a_ts_t)\right] \\ &= \mathbb{E}_{\tau \sim \pi_\theta}\left[ \sum_{t'}^{T1} r_{t'} \cdot \sum_{t=0}^{t'} \nabla_\theta \log \pi_\theta(a_ts_t)\right]. \end{align}\]where \(\tau^{(t')}\) indicates the trajectory up to time \(t'\). (Full disclaimer: I’m not sure if this formalism with \(\tau\) is needed, and I think most people would do this computation without worrying about the precise expectation details.)

Step (iii) follows from a nifty algebra trick. To simplify the subsequent notation, let \(f_t := \nabla_\theta \log \pi_\theta(a_ts_t)\). In addition, ignore the expectation; we’ll only rearrange the inside here. With this substitution and setup, the sum inside the expectation from Step (ii) turns out to be
\[\begin{align} r_0f_0 &+ \\ r_1f_0 &+ r_1f_1 + \\ r_2f_0 &+ r_2f_1 + r_2f_2 + \\ \cdots \\ r_{T1}f_0 &+ r_{T1}f_1 + r_{T1}f_2 \cdots + r_{T1}f_{T1} \end{align}\]In other words, each \(r_{t'}\) has its own row of \(f\)value to which it gets distributed. Next, switch to the column view: instead of summing rowwise, sum columnwise. The first column is \(f_0 \cdot \left(\sum_{t=0}^{T1}r_t\right)\). The second is \(f_1 \cdot \left(\sum_{t=1}^{T1}r_t\right)\). And so on. Doing this means we get the desired formula after replacing \(f_t\) with its real meaning and hitting the expression with an expectation.
Note: it is very easy to make a typo with these. I checked my math carefully and crossreferenced it with references online (which themselves have typos). If any readers find a typo, please let me know.
Using the above formulation, we finally introduce our baseline \(b\), which is a function of \(s_t\) (and not \(s_{t'}\), I believe). We “insert” it inside the term in parentheses:
\[\nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}[R(\tau)] = \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}r_{t'}  b(s_t)\right) \right]\]At first glance, it doesn’t seem like this will be helpful, and one might wonder if this would cause the gradient estimate to become biased. Fortunately, it turns out that this is not a problem. This was surprising to me, because all we know is that \(b(s_t)\) is a function of \(s_t\). However, this is a bit misleading because usually we want \(b(s_t)\) to be the expected return starting at time \(t\), which means it really “depends” on the subsequent time steps. For now, though, just think of it as a function of \(s_t\).
Understanding the Baseline
In this section, I first go over why inserting \(b\) above doesn’t make our gradient estimate biased. Next, I will go over why the baseline reduces variance of the gradient estimate. These two capture the best of both worlds: staying unbiased and reducing variance. In general, any time you have an unbiased estimate and it remains so after applying a variance reduction technique, then apply that variance reduction!
First, let’s show that the gradient estimate is unbiased. We see that with the baseline, we can distribute and rearrange and get:
\[\nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}[R(\tau)] = \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}r_{t'}\right)  \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) b(s_t) \right]\]Due to linearity of expectation, all we need to show is that for any single time \(t\), the gradient of \(\log \pi_\theta(a_ts_t)\) multiplied with \(b(s_t)\) is zero. This is true because
\[\begin{align} \mathbb{E}_{\tau \sim \pi_\theta}\Big[\nabla_\theta \log \pi_\theta(a_ts_t) b(s_t)\Big] &= \mathbb{E}_{s_{0:t},a_{0:t1}}\Big[ \mathbb{E}_{s_{t+1:T},a_{t:T1}} [\nabla_\theta \log \pi_\theta(a_ts_t) b(s_t)]\Big] \\ &= \mathbb{E}_{s_{0:t},a_{0:t1}}\Big[ b(s_t) \cdot \underbrace{\mathbb{E}_{s_{t+1:T},a_{t:T1}} [\nabla_\theta \log \pi_\theta(a_ts_t)]}_{E}\Big] \\ &= \mathbb{E}_{s_{0:t},a_{0:t1}}\Big[ b(s_t) \cdot \mathbb{E}_{a_t} [\nabla_\theta \log \pi_\theta(a_ts_t)]\Big] \\ &= \mathbb{E}_{s_{0:t},a_{0:t1}}\Big[ b(s_t) \cdot 0 \Big] = 0 \end{align}\]Here are my usual overlydetailed comments (apologies in advance):

Note I: this notation is similar to what I had before. The trajectory \(s_0,a_0,\ldots,a_{T1},s_{T}\) is now represented as \(s_{0:T},a_{0:T1}\). In addition, the expectation is split up, which is allowed. If this is confusing, think of the definition of the expectation with respect to at least two variables. We can write brackets in any appropriately enclosed location. Furthermore, we can “omit” the unnecessary variables in going from \(\mathbb{E}_{s_{t+1:T},a_{t:T1}}\) to \(\mathbb{E}_{a_t}\) (see expression \(E\) above). Concretely, assuming we’re in discreteland with actions in \(\mathcal{A}\) and states in \(\mathcal{S}\), this is because \(E\) evaluates to:
\[\begin{align} E &= \sum_{a_t\in \mathcal{A}}\sum_{s_{t+1}\in \mathcal{S}}\cdots \sum_{s_T\in \mathcal{S}} \underbrace{\pi_\theta(a_ts_t)P(s_{t+1}s_t,a_t) \cdots P(s_Ts_{T1},a_{T1})}_{p((a_t,s_{t+1},a_{t+1}, \ldots, a_{T1},s_{T}))} (\nabla_\theta \log \pi_\theta(a_ts_t)) \\ &= \sum_{a_t\in \mathcal{A}} \pi_\theta(a_ts_t)\nabla_\theta \log \pi_\theta(a_ts_t) \sum_{s_{t+1}\in \mathcal{S}} P(s_{t+1}s_t,a_t) \sum_{a_{t+1}\in \mathcal{A}}\cdots \sum_{s_T\in \mathcal{S}} P(s_Ts_{T1},a_{T1})\\ &= \sum_{a_t\in \mathcal{A}} \pi_\theta(a_ts_t)\nabla_\theta \log \pi_\theta(a_ts_t) \end{align}\]This is true because of the definition of expectation, whereby we get the joint density over the entire trajectory, and then we can split it up like we did earlier with the gradient of the log probability computation. We can distribute \(\nabla_\theta \log \pi_\theta(a_ts_t)\) all the way back to (but not beyond) the first sum over \(a_t\). Pushing sums “further back” results in a bunch of sums over densities, each of which sums to one. The astute reader will notice that this is precisely what happens with variable elimination for graphical models. (The more technical reason why “pushing values back through sums” is allowed has to do with abstract algebra properties of the sum function, which is beyond the scope of this post.)

Note II: This proof above also works with an infinitetime horizon. In Appendix B of the Generalized Advantage Estimation paper (arXiv link), the authors do so with a proof exactly matching the above, except that \(T\) and \(T1\) are now infinity.

Note III: About the expectation going to zero, that’s due to a wellknown fact about score functions, which are precisely the gradient of log probailities. We went over this in my STAT 210A class last fall. It’s again the log derivative trick. Observe that:
\[\mathbb{E}_{a_t}\Big[\nabla_\theta \log \pi_\theta(a_ts_t)\Big] = \int \frac{\nabla_\theta \pi_\theta(a_ts_t)}{\pi_{\theta}(a_ts_t)}\pi_{\theta}(a_ts_t)da_t = \nabla_\theta \int \pi_{\theta}(a_ts_t)da_t = \nabla_\theta \cdot 1 = 0\]where the penultimate step follows from how \(\pi_\theta\) is a density. This follows for all time steps, and since the gradient of the log gets distributed for each \(t\), it applies in all time steps. I switched to the continuousland version for this, but it also applies with sums, as I just recently used in Note I.
The above shows that introducing \(b\) doesn’t cause bias.
The last thing to cover is why its introduction reduces variance. I provide an approximate argument. To simplify notation, set \(R_t(\tau) = \sum_{t'=t}^{T1}r_{t'}\). We focus on the inside of the expectation (of the gradient estimate) to analyze the variance. The technical reason for this is that expectations are technically constant (and thus have variance zero) but in practice we have to approximate the expectations with trajectories, and that has high variance.
The variance is approximated as:
\[\begin{align} {\rm Var}\left(\sum_{t=0}^{T1}\nabla_\theta \log \pi_\theta(a_ts_t) (R_t(\tau)b(s_t))\right)\;&\overset{(i)}{\approx}\; \sum_{t=0}^{T1} \mathbb{E}\tau\left[\Big(\nabla_\theta \log \pi_\theta(a_ts_t) (R_t(\tau)b(s_t))\Big)^2\right] \\ \;&{\overset{(ii)}{\approx}}\; \sum_{t=0}^{T1} \mathbb{E}_\tau \left[\Big(\nabla_\theta \log \pi_\theta(a_ts_t)\Big)^2\right]\mathbb{E}_\tau\left[\Big(R_t(\tau)  b(s_t))^2\right] \end{align}\]Approximation (i) is because we are approximating the variance of a sum by computing the sum of the variances. This is not true in general, but if we can assume this, then by the definition of the variance \({\rm Var}(X) := \mathbb{E}[X^2](\mathbb{E}[X])^2\), we are left with the \(\mathbb{E}[X^2]\) term since we already showed that introducing the baseline doesn’t cause bias. Approximation (ii) is because we assume independence among the values involved in the expectation, and thus we can factor the expectation.
Finally, we are left with the term \(\mathbb{E}_{\tau} \left[\Big(R_t(\tau)  b(s_t))^2\right]\). If we are able to optimize our choice of \(b(s_t)\), then this is a least squares problem, and it is well known that the optimal choice of \(b(s_t)\) is to be the expected value of \(R_t(\tau)\). In fact, that’s why policy gradient researchers usually want \(b(s_t) \approx \mathbb{E}[R_t(\tau)]\) to approximate the expected return starting at time \(t\), and that’s why in the vanilla policy gradient algorithm we have to refit the baseline estimate each time to make it as close to the expected return \(\mathbb{E}[R_t(\tau)]\). At last, I understand.
How accurate are these approximations in practice? My intuition is that they are actually fine, because recent advances in reinforcement learning algorithms, such as A3C, focus on the problem of breaking correlation among samples. If the correlation among samples is broken, then Approximation (i) becomes better, because I think the samples \(s_0,a_0,\ldots,a_{T1},s_{T}\) are no longer generated from the same trajectory.
Well, that’s my intuition. If anyone else has a better way of describing it, feel free to let me know in the comments or by email.
Discount Factors
So far, we have assumed we wanted to optimize the expected return, or the expected sum of rewards. However, if you’ve studied value iteration and policy iteration, you’ll remember that we usually use discount factors \(\gamma \in (0,1]\). These empirically work well because the effect of an action many time steps later is likely to be negligible compared to other action. Thus, it may not make sense to try and include raw distant rewards in our optimization problem. Thus, we often impose a discount as follows:
\[\begin{align} \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}[R(\tau)] &= \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}r_{t'}  b(s_t)\right) \right] \\ &\approx \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}\gamma^{t't}r_{t'}  b(s_t)\right) \right] \end{align}\]where the \(\gamma^{t't}\) serves as the discount, starting from 1, then getting smaller as time passes. (The first line above is a repeat of the policy gradient formula that I describe earlier.) As this is not exactly the “desired” gradient, this is an approximation, but it’s a reasonable one. This time, we now want our baseline to satisfy \(b(s_t) \approx \mathbb{E}[r_t + \gamma r_{t+1} + \cdots + \gamma^{T1t} r_{T1}]\).
Advantage Functions
In this final section, we replace the policy gradient formula with the following value functions:
\[Q^\pi(s,a) = \mathbb{E}_{\tau \sim \pi_\theta}\left[\sum_{t=0}^{T1} r_t \;\Bigg\; s_0=s,a_0=a\right]\] \[V^\pi(s) = \mathbb{E}_{\tau \sim \pi_\theta}\left[\sum_{t=0}^{T1} r_t \;\Bigg\; s_0=s\right]\]Both of these should be familiar from basic AI; see the CS 188 notes from Berkeley if this is unclear. There are also discounted versions, which we can denote as \(Q^{\pi,\gamma}(s,a)\) and \(V^{\pi,\gamma}(s)\). In addition, we can also consider starting at any given time step, as in \(Q^{\pi,\gamma}(s_t,a_t)\) which provides the expected (discounted) return assuming that at time \(t\), our stateaction pair is \((s_t,a_t)\).
What might be new is the advantage function. For the undiscounted version, it is defined simply as:
\[A^\pi(s,a) = Q^\pi(s,a)  V^\pi(s)\]with a similar definition for the discounted version. Intuitively, the advantage tells us how much better action \(a\) would be compared to the return based on an “average” action.
The above definitions look very close to what we have in our policy gradient formula. In fact, we can claim the following:
\[\begin{align} \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}[R(\tau)] &= \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \left(\sum_{t'=t}^{T1}r_{t'}  b(s_t)\right) \right] \\ &{\overset{(i)}{=}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \cdot \Big(Q^{\pi}(s_t,a_t)V^\pi(s_t)\Big) \right] \\ &{\overset{(ii)}{=}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \cdot A^{\pi}(s_t,a_t) \right] \\ &{\overset{(iii)}{\approx}}\; \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^{T1} \nabla_\theta \log \pi_\theta(a_ts_t) \cdot A^{\pi,\gamma}(s_t,a_t) \right] \end{align}\]In (i), we replace terms with their expectations. This is not generally valid to do, but it should work in this case. My guess is that if you start from the second line above (after the “(i)”) and plug in the definition of the expectation inside and rearrange terms, you can get the first line. However, I have not had the time to check this in detail and it takes a lot of space to write out the expectation fully. The conditioning with the value functions makes it a bit messy and thus the law of iterated expectation may be needed.
Also from line (i), we notice that the value function is a baseline, and hence we can add it there without changing the unbiasedness of the expectation. Then lines (ii) and (iii) are just for the advantage function. The implication of this formula is that the problem of policy gradients, in some sense, reduces to finding good estimates \(\hat{A}^{\pi,\gamma}(s_t,a_t)\) of the advantage function \(A^{\pi,\gamma}(s_t,a_t)\). That is precisely the topic of the paper Generalized Advantage Estimation.
Concluding Remarks
Hopefully, this is a helpful, selfcontained, bareminimum introduction to policy gradients. I am trying to learn more about these algorithms, and going through the math details is helpful. This will also make it easier for me to understand the increasing number of research papers that are using this notation.
I also have to mention: I remember a few years ago during the first iteration of CS 294112 that I had no idea how policy gradients worked. Now, I think I have become slightly more enlightened.
Acknowledgements: I thank John Schulman for making his notes publicly available.
Update April 19, 2017: I have code for vanilla policy gradients in my reinforcement learning GitHub repository.
Keeping Track of Research Articles: My Paper Notes Repository
The number of research papers in Artificial Intelligence has reached unmanageable proportions. Conferences such as ICML, NIPS, and ICLR others are getting record amounts of paper submissions. In addition, tens of AIrelated papers get uploaded to arXiv every weekday. With all these papers, it can be easy to feel lost and overwhelmed.
Like many researchers, I think I do not read enough research papers. This year, I resolved to change that, so I started an opensource GitHub repository called “Paper Notes” where I list papers that I’ve read along with my personal notes and summaries, if any. Papers without such notes are currently on my TODO radar.
After almost three months, I’m somewhat pleased with my reading progress. There are a healthy number of papers (plus notes) listed, arranged by subject matter and then further arranged by year. Not enough for me, but certainly not terrible either.
I was inspired to make this by seeing Denny Britz’s similar repository, along with Adrian Colyer’s blog. My repository is similar to Britz’s, though my aim is not to list all papers in Deep Learning, but to write down the ones that I actually plan to read at some point. (I see other repositories where people simply list Deep Learning papers without notes, which seems pretty pointless to me.) Colyer’s blog posts represent the kind of notes that I’d like to take for each paper, but I know that I can’t dedicate that much time to finetuning notes.
Why did I choose GitHub as the backend for my paper management, rather than something like Mendeley? First, GitHub is the default place where (pretty much) everyone in AI puts their opensource stuff: blogs, code, you name it. I’m already used to GitHub, so Mendeley would have to provide some serious benefit for me to switch over. I also don’t need to use advanced annotation and organizing materials, given that the top papers are easily searchable online (including their BibTeX references). In addition, by making my Paper Notes repository online, I can show this as evidence to others that I’m reading papers. Maybe this will even impress a few folks, and I say this only because everyone wants to be noticed in some way; that’s partly Colyer’s inspiration for his blog. So I think, on balance, it will be useful for me to keep updating this repository.
What Biracial People Know
There’s an opinion piece in the New York Times by Moises VelasquezManoff which talks about (drum roll please) biracial people. As he mentions:
Multiracials make up an estimated 7 percent of Americans, according to the Pew Research Center, and they’re predicted to grow to 20 percent by 2050.
Thus, I suspect that sometime in the next few decades, we will start talking about race in terms of precise racial percentages, such as “100 percent White” or in rarer cases, “25 percent White, 25 percent Asian, 25 percent Black, and 25 percent Native American.” (Incidentally, I’m not sure why the article uses “Biracial” when “Multiracial” would clearly have been a more appropriate term; it was likely due to the Barack Obama factor.)
The phrase “precise racial percentages” is misleading. Since all humans came from the same ancestor, at some point in history we must have been “one race.” For the sake of defining these racial percentages, we can take a date — say 4000BC — when, presumably, the various races were sufficiently different, ensconced in their respective geographic regions, and when interracial marriages (or rape) was at a minimum. All humans alive at that point thus get a “100 percent [insert_race_here]” attached to them, and we do the arithmetic from there.
What usually happens in practice, though, is that we often default to describing one part of one race, particularly with people who are \(X\) percent Black, where \(X > 0\). This is a relic of the embarrassing “One Drop Rule” the United States had, but for now it’s probably — well, I hope — more for selfselecting racial identity.
Listing precise racial percentages would help us better identify people who are not easy to immediately peg in racial categories, which will increasingly become an issue as more and more multiracial people like me blur the lines between the races. In fact, this is already a problem for me even with singlerace people: I sometimes cannot distinguish between Hispanics versus Whites. For instance, I thought Ted Cruz and Marco Rubio were 100 percent White.
Understanding race is also important when considering racial diversity and various ethical or sensitive questions over who should get “preferences.” For instance, I wonder if people label me as a “privileged white male” or if I get a pass for being biracial? Another question: for a job at a firm which has had a history of racial discrimination and is trying to make up for that, should the applicant who is 75 percent Black, 25 percent White, get a hair’s preference versus someone who is 25 percent Black and 75 percent White? Would this also apply if they actually have very similar skin color?
In other words, does one weigh more towards the looks or the precise percentages? I think the precise percentages method is the way schools, businesses, and government operate, despite how this isn’t the case in casual conversations.
Anyway, these are some of the thoughts that I have as we move towards a more racially diverse society, as multiracial people cannot have singlerace children outside of adoption.
Back to the article: as one would expect, it discusses the benefits of racial diversity. I can agree with the following passage:
Social scientists find that homogeneous groups like [Donald Trump’s] cabinet can be less creative and insightful than diverse ones. They are more prone to groupthink and less likely to question faulty assumptions.
The caveat is that this assumes the people involved are equally qualified; a racially homogeneous (in whatever race), but extremely welleducated cabinet would be much better than a racially diverse cabinet where no one even finished high school. But controlling for quality, I can agree.
Diversity also benefits individuals, as the author notes. It is here where Mr. VelasquezManoff points out that Barack Obama was not just Black, but also biracial, which may have benefited his personal development. Multiracials make up a large fraction of the population in racially diverse Hawaii, where Obama was born (albeit, probably with more AsianWhite overlap).
Yes, I agree that diversity is important for a variety of reasons. It is not easy, however:
It’s hard to know what to do about this except to acknowledge that diversity isn’t easy. It’s uncomfortable. It can make people feel threatened. “We promote diversity. We believe in diversity. But diversity is hard,” Sophie Trawalter, a psychologist at the University of Virginia, told me.
That very difficulty, though, may be why diversity is so good for us. “The pain associated with diversity can be thought of as the pain of exercise,” Katherine Phillips, a senior vice dean at Columbia Business School, writes. “You have to push yourself to grow your muscles.”
I cannot agree more.
Moving on:
Closer, more meaningful contact with those of other races may help assuage the underlying anxiety. Some years back, Dr. Gaither of Duke ran an intriguing study in which incoming white college students were paired with either samerace or differentrace roommates. After four months, roommates who lived with different races had a more diverse group of friends and considered diversity more important, compared with those with samerace roommates. After six months, they were less anxious and more pleasant in interracial interactions.
Ouch, this felt like a blindsiding attack, and is definitely my main gripe with this article. In college, I had two roommates, both of whom have a different racial makeup than me. They both seemed to be relatively popular and had little difficulty mingling with a diverse group of students. Unfortunately, I certainly did not have a “diverse group of friends.” After all, if there was a prize for college for “least popular student” I would be a perennial contender. (As incredible as it may sound, in high school, where things were worse for me, I can remember a handful of people who might have been even lower on the social hierarchy.)
Well, I guess what I want to say is that, this attack notwithstanding, Mr. VelasquezManoff’s article brings up interesting and reasonably accurate points about biracial people. At the very least, he writes about concepts which are sometimes glossed over or underappreciated nowadays in our discussions about race.
Understanding Generative Adversarial Networks
Over the last few weeks, I’ve been learning more about some mysterious thing called Generative Adversarial Networks (GANs). GANs originally came out of a 2014 NIPS paper (read it here) and have had a remarkable impact on machine learning. I’m surprised that, until I was the TA for Berkeley’s Deep Learning class last semester, I had never heard of GANs before.^{1}
They certainly haven’t gone unnoticed in the machine learning community, though. Yann LeCun, one of the leaders in the Deep Learning community, had this to say about them during his Quora session on July 28, 2016:
The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This is an idea that was originally proposed by Ian Goodfellow when he was a student with Yoshua Bengio at the University of Montreal (he since moved to Google Brain and recently to OpenAI).
This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.
If he says something like that about GANs, then I have no excuse for not learning about them. Thus, I read what is probably the highestquality general overview available nowadays: Ian Goodfellow’s tutorial on arXiv, which he then presented in some form at NIPS 2016. This was really helpful for me, and I hope that later, I can write something like this (but on another topic in AI).
I won’t repeat what GANs can do here. Rather, I’m more interested in knowing how GANs are trained. Following now are some of the most important insights I gained from reading the tutorial:

Major Insight 1: the discriminator’s loss function is the cross entropy loss function. To understand this, let’s suppose we’re doing some binary classification with some trainable function \(D: \mathbb{R}^n \to [0,1]\) that we wish to optimize, where \(D\) indicates the estimated probability of some data point \(x_i \in \mathbb{R}^n\) being in the first class. To get the predicted probability of being in the second class, we just do \(1D(x_i)\). The output of \(D\) must therefore be constrained in \([0,1]\), which is easy to do if we tack on a sigmoid layer at the end. Furthermore, let \((x_i,y_i) \in (\mathbb{R}^n, \{0,1\})\) be the inputlabel pairing for training data points.
The cross entropy between two distributions, which we’ll call \(p\) and \(q\), is defined as
\[H(p,q) := \sum_i p_i \log q_i\]where \(p\) and \(q\) denote a “true” and an “empirical/estimated” distribution, respectively. Both are discrete distributions, hence we can sum over their individual components, denoted with \(i\). (We would need to have an integral instead of a sum if they were continuous.) Above, when I refer to a “distribution,” it means with respect to a single training data point, and not the “distribution of training data points.” That’s a different concept.
To apply this loss function to the current binary classification task, for (again) a single data point \((x_i,y_i)\), we define its “true” distribution as \(\mathbb{P}[y_i = 0] = 1\) if \(y_i=0\), or \(\mathbb{P}[y_i = 1] = 1\) if \(y_i=1\). Putting in 2D vector form, it’s either \([1,0]\) or \([0,1]\). Intuitively, we know for sure which class this belongs to (because it’s part of the training data), so it makes sense for a probability distribution to be a “onehot” vector.
Thus, for one data point \(x_1\) and its label, we get the following loss function, where here I’ve changed the input to be more precise:
\[H((x_1,y_1),D) =  y_1 \log D(x_1)  (1y_1) \log (1D(x_1))\]Let’s look at the above function. Notice that only one of the two terms is going to be zero, depending on the value of \(y_1\), which makes sense since it’s defining a distribution which is either \([0,1]\) or \([1,0]\). The other part is the estimated distribution from \(D\). In both cases (the true and predicted distributions) we are encoding a 2D distribution with one value, which lets us treat \(D\) as a realvalued function.
That was for one data point. Summing over the entire dataset of \(N\) elements, we get something that looks like this:
\[H((x_i,y_i)_{i=1}^N,D) =  \sum_{i=1}^N y_i \log D(x_i)  \sum_{i=1}^N (1y_i) \log (1D(x_i))\]In the case of GANs, we can say a little more about what these terms mean. In particular, our \(x_i\)s only come from two sources: either \(x_i \sim p_{\rm data}\), the true data distribution, or \(x_i = G(z)\) where \(z \sim p_{\rm generator}\), the generator’s distribution, based on some input code \(z\). It might be \(z \sim {\rm Unif}[0,1]\) but we will leave it unspecified.
In addition, we also want exactly half of the data to come from these two sources.
To apply this to the sum above, we need to encode this probabilistically, so we replace the sums with expectations, the \(y_i\) labels with \(1/2\), and we can furthermore replace the \(\log (1D(x_i))\) term with \(\log (1D(G(z)))\) under some sampled code \(z\) for the generator. We get
\[H((x_i,y_i)_{i=1}^\infty,D) =  \frac{1}{2} \mathbb{E}_{x \sim p_{\rm data}}\Big[ \log D(x)\Big]  \frac{1}{2} \mathbb{E}_{z} \Big[\log (1D(G(z)))\Big]\]This is precisely the loss function for the discriminator, \(J^{(J)}\).

Major Insight 2: understanding how gradient saturation may or may not adversely affect training. Gradient saturation is a general problem when gradients are too small (i.e. zero) to perform any learning. See Stanford’s CS 231n notes on gradient saturation here for more details. In the context of GANs, gradient saturation may happen due to poor design of the generator’s loss function, so this “major insight” of mine is also based on understanding the tradeoffs among different loss functions for the generator. This design, incidentally, is where we can be creative; the discriminator needs the cross entropy loss function above since it has a very specific function (to discriminate among two classes) and the cross entropy is the “best” way of doing this.
Using Goodfellow’s notation, we have the following candidates for the generator loss function, as discussed in the tutorial. The first is the minimax version:
\[J^{(G)} = J^{(J)} = \frac{1}{2} \mathbb{E}_{x \sim p_{\rm data}}\Big[ \log D(x)\Big] + \frac{1}{2} \mathbb{E}_{z} \Big[\log (1D(G(z)))\Big]\]The second is the heuristic, nonsaturating version:
\[J^{(G)} = \frac{1}{2}\mathbb{E}_z\Big[\log D(G(z))\Big]\]Finally, the third is the maximum likelihood version:
\[J^{(G)} = \frac{1}{2}\mathbb{E}_z\left[e^{\sigma^{1}(D(G(z)))}\right]\]What are the advantages and disadvantages of these generator loss functions? For the minimax version, it’s simple and allows for easier theoretical results, but in practice its not that useful, due to gradient saturation. As Goodfellow notes:
In the minimax game, the discriminator minimizes a crossentropy, but the generator maximizes the same crossentropy. This is unfortunate for the generator, because when the discriminator successfully rejects generator samples with high confidence, the generator’s gradient vanishes.
As suggested in Chapter 3 of Michael Nielsen’s excellent online book, the crossentropy is a great loss function since it is designed in part to accelerate learning and avoid gradient saturation only up to when the classifier is correct (since we don’t want the gradient to move in that case!).
I’m not sure how to clearly describe this formally. For now, I will defer to Figure 16 in Goodfellow’s tutorial (see the top of this blog post), which nicely shows the value of \(J^{(G)}\) as a function of the discriminator’s output, \(D(G(z))\). Indeed, when the discriminator is winning, we’re at the left side of the graph, since the discriminator outputs the probability of the sample being from the true data distribution.
By the way, why is \(J^{(G)} = J^{(J)}\) only a function of \(D(G(z))\) as suggested by the figure? What about the other term in \(J^{(J)}\)? Notice that of the two terms in the loss function, the first one is only a function of the discriminator’s parameters! The second part, which uses the \(D(G(z))\) term, depends on both \(D\) and \(G\). Hence, for the purposes of performing gradient descent with respect to the parameters of \(G\), only the second term in \(J^{(J)}\) matters; the first term is a constant that disappears after taking derivatives \(\nabla_{\theta^{(G)}}\).
The figure makes it clear that the generator will have a hard time doing any sort of gradient update at the left portion of the graph, since the derivatives are close to zero. The problem is that the left portion of the graph represents the most common case when starting the game. The generator, after all, starts out with basically random parameters, so the discriminator can easily tell what is real and what is fake.^{2}
Let’s move on to the other two generator cost functions. The second one, the heuristicallymotivated one, uses the idea that the generator’s gradient only depends on the second term in \(J^{(J)}\). Instead of flipping the sign of \(J^{(J)}\), they instead flip the target: changing \(\log (1D(G(z)))\) to \(\log D(G(z))\). In other words, the “sign flipping” happens at a different part, so the generator still optimizes something “opposite” of the discriminator. From this reformulation, it appears from the figure above that \(J^{(G)}\) now has desirable gradients in the left portion of the graph. Thus, the advantage here is that the generator gets a strong gradient signal so that it can quickly improve. The downside is that it’s not easier to analyze, but who cares?
Finally, the maximum likelihood cost function has the advantage of being motivated based on maximum likelihood, which by itself has a lot of desirable properties. Unfortunately, the figure above shows that it has a flat slope in the left portion, though it seems to be slightly better than the minimax version since it decreases rapidly “sooner.” Though that might not be an “advantage,” since Goodfellow warns about high variance. That might be worth thinking about in more detail.
One last note: the function \(J^{(G)}\), at least for the three cost functions here, does not depend directly on \(x\) at all! That’s interesting … and in fact, Goodfellow argues that makes GANs resistant to overfitting since it can’t copy from \(x\).
I wish more tutorials like this existed for other AI concepts. I particularly enjoyed the three exercises and the solutions within this tutorial on GANs. I have more detailed notes here in my Paper Notes GitHub repository (I should have started this repository back in 2013). I highly recommend this tutorial to anyone wanting to know more about GANs.
Update March 26, 2020: made a few clarifications.

Ian Goodfellow, the lead author on the GANs paper, was a guest lecture for the class, where (obviously) he talked about GANs. ↩

Actually, the discriminator also starts out random, right? I think the discriminator has an easier job, though, since supervised learning is easier than generating realistic images (I mean, c’mon??) so perhaps the discriminator simply learns faster, and the generator has to spend a lot of time catching up. ↩
My Thoughts on CS 231n Being Forced To Take Down Videos
CS 231n: Convolutional Neural Networks for Visual Recognition is, in my biased opinion, one of the most important and thrilling courses offered by Stanford University. It has been taught twice so far and will appear again in the upcoming Spring quarter.
Due to its popularity, the course lectures for the second edition (Winter 2016) were videotaped and released online. This is not unusual among computer science graduate level courses due to high demand both inside and outside the university.
Unfortunately, as discussed in this rather large reddit discussion thread, Andrej Karpathy (one of the three instructors) was forced to pull down the lecture videos. He later clarified on his Twitter account that the reason had to do with the lack of captioning/subtitles in the lecture videos, which relates to a news topic I blogged about just over two years ago.
If you browse the reddit thread, you will see quite a lot of unhappy students. I just joined reddit and I was hoping to make a comment there, but reddit disables posting after six months. And after thinking about it, I thought it would make more sense to write some brief thoughts here instead.
To start, I should state upfront that I have no idea what happened beyond the stuff we can all read online. I don’t know who made the complaint, what the course staff did, etc.
Here’s my stance regarding class policies on watching videos:
If a class requires watching videos for whatever reason, then that video should have subtitles. Otherwise, no such action is necessary, though the course staff should attempt as much as is reasonable to have subtitles for all videos.
I remember two times when I had to face this problem of watching a nonsubtitled video as a homework assignment: in an introductory Women’s, Gender, and Sexuality Studies course and an Africana Studies class about black athletes. For the former, we were assigned to watch a video about a transgender couple, and for the latter, the video was about black golfers. In both cases, the professors gave me copies of the movie (other students didn’t get these) and I watched one in a room myself with the volume cranked up and the other one with another person who told me what was happening.
Is that ideal? Well, no. To (new) readers of this blog, welcome to the story of my life!
More seriously, was I supposed to do something about it? The professors didn’t make the videos, which were a tiny portion of the overall courses. I didn’t want to get all up in arms about this, so in both cases, I brought it up with them and they understood my situation (and apologized).
Admittedly, my brief stance above is incomplete and belies a vast gray area. What if students are given the option of doing one of two “required” assignments: watching a video or reading a book? That’s a gray area, though I would personally lean that towards “required viewing” and thus “required subtitles.”
Class lecture videos also fall in a gray area. They are not required viewing, because students should attend lectures in person. Unfortunately, the lack of subtitles for these videos definitely puts deaf and hard of hearing students like myself at a disadvantage. I’ve lost count of the amount of lectures that I wish I could have rewatched, but it extraordinarily difficult for me to do so for nonsubtitled videos.
Ultimately, however, as long as I can attend lectures and understand some of the material, I do not worry about whether lecture videos have subtitles. Just about every videotaped class that I have taken did not have subtitled lecture videos, with one exception: CS 267 from Spring 2016, after I had negotiated about it with Berkeley’s DSP.
Heck, the CS 294129 class which I TAed for last semester — which is based on CS 231n! — had lecture videos. Were there captions? Nope.
Am I frustrated? Yes, but it’s understandable frustration due to the cost of adding subtitles. As a similar example, I’m frustrated at the identity politics practiced by the Democratic party, but it’s understandable frustration due to what political science instructs us to do, which is why I’m not planning to jump ship to another party.
Thus in my case, if I were a student in CS 231n, I would not be inclined to pressure the staff to pull the videos down. Again, this comes with the obvious caveat; I don’t know the situation and it might have been worse than I imagine.
As this discussion would imply, I don’t like pulling down lecture videos as “collateral damage”.^{1} I worry, however, if that’s in part because I’m too timid. Hypothetically and broadly speaking, if I have to take out my frustration (e.g. with lawsuits) on certain things, I don’t want to do this for something like lecture videos, which would make a number of folks angry at me, whether or not they openly express it.
On a more positive note … it turns out that, actually, the CS 231n lecture videos are online! I’m not sure why, but I’m happy. Using YouTube’s automatic captions, I watched one of the lectures and finally understood a concept that was critical and essential for me to know when I was writing my latest technical blog post.
Moreover, the automatic captions are getting better and better each year. They work pretty well on Andrej, who has a slight accent (Russian?). I dislike attending research talks if I don’t understand what’s going on, but given that so many are videotaped these days, whether at Berkeley or at conferences, maybe watching them offline is finally becoming a viable alternative.

In another case where lecture videos had to be removed, consider MIT’s Open Courseware and Professor Walter Lewin’s famous physics lectures. MIT removed the videos after it was found that Lewin had sexually harassed some of his students. Lewin’s harassment disgusted me, but I respectfully disagreed with MIT’s position about removing his videos, siding with thenMIT professor Scott Aaronson. In an infamous blog post, Professor Aaronson explained why he opposed the removal of the videos, which subsequently caused him to be the subject of a haterage/attack. Consequently, I am now a permanent reader of his blog. ↩
These Aren't Your Father's Hearing Aids
I am now wearing Oticon Dynamo hearing aids. The good news is that I’ve run many times with them and so far have not had issues with water resistance.
However, I wanted to bring up a striking point that really made me realize about how our world has changed remarkably in the last few years.
A few months ago, when I was first fitted with the hearing aids, my audiologist set the default volume level to be “on target” for me. The hearing aid is designed to provide different amounts of power to people depending on their raw hearing level. There’s a volume control on it which goes from “1” (weak) to “4” (powerful), which I can easily adjust as I wish. The baseline setting is “3”, but this baseline is what audiologist adjust on a casebycase basis. This means my “3” (and thus, my “1” and “4” settings) may be more powerful, less powerful, or the same compared to the respective settings for someone else.
When my audiologist first fit the hearing aids for me, I felt that my left hearing aid was too quiet and my right one too loud by default, so she modified the baselines.
She also, critically, gave me about a week to adjust to the hearing aids, and I was to report back on whether its strength was correctly set.
During that week, I wore the hearing aids, but I then decided that I was originally mistaken about both hearing aids, since I had to repeatedly increase the volume for the left one and decrease the volume for the right one.
I reported back to my audiologist and said that she was right all along, and that my baselines needed to be back to their default levels. She was able to corroborate my intuition by showing me — amazingly – how often I had adjusted the hearing aid volume level, and in which direction.
Hearing aids are, apparently, now fitted with these advanced sensors so they can track exactly how you adjust them (volume controls or otherwise).
The lesson is that just about everything nowadays consists of sensors, a point which is highlighted in Thomas L. Friedman’s excellent book Thank You for Being Late. It is also a characteristic of what computer scientists refer to as the “Internet of Things.”
Obviously, these certainly aren’t the hearing aids your father wore when he was young.
Academics Against Immigration Executive Order
I just signed a petition, Academics Against Immigration Executive Order to oppose the Trump administration’s recent executive order. You can find the full text here along with the names of those who have signed up. (Graduate students are in the “Other Signatories” category and may take a while to update.) I like this petition because it clearly lists the names of people so as to avoid claims of duplication and/or bogus signatures for anonymous petitions. There are lots of academic superstars on the list, including (I’m proud to say) my current statistics professor Michael I. Jordan and my statistics professor William Fithian from last semester.
The petition lists three compelling reasons to oppose the order, but let me just chime in with some extra thoughts.
I understand the need to keep our country safe. But in order to do so, there has to be a correct tradeoff in terms of security versus profiling (for lack of a better word) and in terms of costs versus benefits.
On the spectrum of security, to one end are those who deny the existence of radical Islam and the impact of religion on terrorism. On the other end are those who would happily ban an entire religion and place the blame and burden on millions of lawabiding people fleeing oppression. This order is far too close to the second end.
In terms of costs and benefits, I find an analogy to policing useful. Mayors and police chiefs shouldn’t be assigning their police officers uniformly throughout cities. The police should be targeted in certain hotspots of crime as indicated by past trends. That’s the most logical and costeffective way to crack down on crime.
Likewise, if were are serious about stopping radical Islamic terrorism, putting a blanket ban on Muslims is like the “uniform policing strategy” and will also cause additional problems since Muslims would (understandably!) feel unfairly targeted. For instance, Iran is already promising “proportional responses”. I also have to mention that the odds of being killed by a refugee terrorist are so low that the amount of anxiety towards them does not justify the cost.
By the way, I’m still waiting for when Saudi Arabia — the source of 15 out of 19 terrorists responsible for 9/11 — gets on the executive order list. I guess President Trump has business dealings there? (Needless to say, that’s why conflict of interest laws exist.)
I encourage American academics to take a look at this order and (hopefully) sign the petition. I also urge our Secretary of Defense, James Mattis, to talk to Trump and get him to rescind and substantially revise the order. While I didn’t state this publicly to anyone, I have more respect for Mattis than any one else in the Trump cabinet, and hopefully that will remain the case.
Understanding Higher Order Local Gradient Computation for Backpropagation in Deep Neural Networks
Introduction
One of the major difficulties in understanding how neural networks work is due to the backpropagation algorithm. There are endless texts and online guides on backpropagation, but most are useless. I read several explanations of backpropagation when I learned about it from 2013 to 2014, but I never felt like I really understood it until I took/TAed the Deep Neural Networks class at Berkeley, based on the excellent Stanford CS 231n course.
The course notes from CS 231n include a tutorial on how to compute gradients for local nodes in computational graphs, which I think is key to understanding backpropagation. However, the notes are mostly for the onedimensional case, and their main advice for extending gradient computation to the vector or matrix case is to keep track of dimensions. That’s perfectly fine, and in fact that was how I managed to get through the second CS 231n assignment.
But this felt unsatisfying.
For some of the harder gradient computations, I had to test several different ideas before passing the gradient checker, and sometimes I wasn’t even sure why my code worked! Thus, the purpose of this post is to make sure I deeply understand how gradient computation works.
Note: I’ve had this post in draft stage for a long time. However, I just found out that the notes from CS 231n have been updated with a guide from Erik LearnedMiller on taking matrix/vector derivatives. That’s worth checking out, but fortunately, the content I provide here is mostly distinct from his material.
The Basics: Computational Graphs in One Dimension
I won’t belabor the details on onedimensional graphs since I assume the reader has read the corresponding Stanford CS 231n guide. Another nice post is from Chris Olah’s excellent blog. For my own benefit, I reviewed derivatives on computational graphs by going through the CS 231n example with sigmoids (but with the sigmoid computation spread out among finergrained operations). You can see my handwritten computations in the following image. Sorry, I have absolutely no skill in getting this up quickly using tikz, Inkscape, or other visualization tactics/software. Feel free to rightclick and open the image in a new tab. Warning: it’s big. (But I have to say, the iPhone7 plus makes really nice images. I remember the good old days when we had to take our cameras to CVS to get them developed…)
Another note: from the image, you can see that this is from the fourth lecture of CS 231n class. I watched that video on YouTube, which is excellent and of highquality. Fortunately, there are also automatic captions which are highly accurate. (There’s an archived reddit thread discussing how Andrej Karpathy had to take down the videos due to a related lawsuit I blogged about earlier, but I can see them just fine. Did they get back up somehow? I’ll write more about this at a later date.)
When I was going through the math here, I came up with several rules to myself:

There’s a lot of notation that can get confusing, so for simplicity, I always denoted inputs as \(f_1,f_2,\ldots\) and outputs as \(g_1,g_2,\ldots\), though in this example, we only have one output at each step. By doing this, I can view the \(g_1\)s as a function of the \(f_i\) terms, so the local gradient turns into \(\frac{\partial g_1}{\partial f_i}\) and then I can substitute \(g_1\) in terms of the inputs.

When doing backpropgation, I analyzed it nodebynode, and the boxes I drew in my image contain a number which indicates the order I evaluated them. (I skipped a few repeat blocks just as the lecture did.) Note that when filling in my boxes, I only used the node and any incoming/outgoing arrows. Also, the \(f_i\) and \(g_i\) keep getting repeated, i.e. the next step will have \(g_i\) equal to whatever the \(f_i\) was in the previous block.

Always remember that when we have arrows here, the part above the arrow contains the value of \(f_i\) (respectively, \(g_i\)) and below the arrow we have \(\frac{\partial L}{\partial f_i}\) (respectively \(\frac{\partial L}{\partial g_i}\)).
Hopefully this will be helpful to beginners using computational graphs.
Vector/Matrix/Tensor Derivatives, With Examples
Now let’s get to the big guns — vectors/matrices/tensors. Vectors are a special case of matrices, which are a special case of tensors, the most generalized \(n\)dimensional array. For this section, I will continue using the “partial derivative” notation \(\frac{\partial}{\partial x}\) to represent any derivative form (scalar, vector, or matrix).
ReLU
Our first example will be with ReLUs, because that was covered a bit in the
CS 231n lecture. Let’s suppose \(x \in \mathbb{R}^3\), a 3D column vector
representing some data from a hidden layer deep into the network. The ReLU
operation’s forward pass is extremely simple: \(y = \max\{0,x\}\), which can be
vectorized using np.max
.
The backward pass is where things get tricky. The input is a 3D vector, and so is the output! Hence, taking the derivative of the function \(y(x): \mathbb{R}^3\to \mathbb{R}^3\) means we have to consider the effect of every \(x_i\) on every \(y_j\). The only way that’s possible is to use Jacobians. Using the example here, denoting the derivative as \(\frac{\partial y}{\partial x}\) where \(y(x)\) is a function of \(x\), we have:
\[\begin{align*} \frac{\partial y}{\partial x} &= \begin{bmatrix} \frac{\partial y_1}{\partial x_1} &\frac{\partial y_1}{\partial x_2} & \frac{\partial y_1}{\partial x_3}\\ \frac{\partial y_2}{\partial x_1} &\frac{\partial y_2}{\partial x_2} & \frac{\partial y_2}{\partial x_3}\\ \frac{\partial y_3}{\partial x_1} &\frac{\partial y_3}{\partial x_2} & \frac{\partial y_3}{\partial x_3} \end{bmatrix}\\ &= \begin{bmatrix} \frac{\partial}{\partial x_1}\max\{0,x_1\} &\frac{\partial}{\partial x_2}\max\{0,x_1\} & \frac{\partial}{\partial x_3}\max\{0,x_1\}\\ \frac{\partial}{\partial x_1}\max\{0,x_2\} &\frac{\partial}{\partial x_2}\max\{0,x_2\} & \frac{\partial}{\partial x_3}\max\{0,x_2\}\\ \frac{\partial}{\partial x_1}\max\{0,x_3\} &\frac{\partial}{\partial x_2}\max\{0,x_3\} & \frac{\partial}{\partial x_3}\max\{0,x_3\} \end{bmatrix}\\ &= \begin{bmatrix} 1\{x_1>0\} & 0 & 0 \\ 0 & 1\{x_2>0\} & 0 \\ 0 & 0 & 1\{x_3>0\} \end{bmatrix} \end{align*}\]The most interesting part of this happens when we expand the Jacobian and see that we have a bunch of derivatives, but they all evaluate to zero on the offdiagonal. After all, the effect (i.e. derivative) of \(x_2\) will be zero for the function \(\max\{0,x_3\}\). The diagonal term is only slightly more complicated: an indicator function (which evaluates to either 0 or 1) depending on the outcome of the ReLU. This means we have to cache the result of the forward pass, which easy to do in the CS 231n assignments.
How does this get combined into the incoming (i.e. “upstream”) gradient, which is a vector \(\frac{\partial L}{\partial y}\). We perform a matrix times vector operation with that and our Jacobian from above. Thus, the overall gradient we have for \(x\) with respect to the loss function, which is what we wanted all along, is:
\[\frac{\partial L}{\partial x} = \begin{bmatrix} 1\{x_1>0\} & 0 & 0 \\ 0 & 1\{x_2>0\} & 0 \\ 0 & 0 & 1\{x_3>0\} \end{bmatrix} \cdot \frac{\partial L}{\partial y}\]This is as simple as doing mask * y_grad
where mask
is a numpy array with 0s
and 1s depending on the value of the indicator functions, and y_grad
is the
upstream derivative/gradient. In other words, we can completely bypass the
Jacobian computation in our Python code! Another option is to use y_grad[x <=
0] = 0
, where x
is the data that was passed in the forward pass (just before
ReLU was applied). In numpy, this will set all indices to which the condition x
<= 0
is true to have zero value, precisely clearing out the gradients where we
need it cleared.
In practice, we tend to use minibatches of data, so instead of a single \(x
\in \mathbb{R}^3\), we have a matrix \(X \in \mathbb{R}^{3 \times n}\) with
\(n\) columns.^{1} Denote the \(i\)th column as \(x^{(i)}\). Writing out
the full Jacobian is too cumbersome in this case, but to visualize it, think of
having \(n=2\) and then stacking the two samples \(x^{(1)},x^{(2)}\) into a
sixdimensional vector. Do the same for the output \(y^{(1)},y^{(2)}\). The
Jacobian turns out to again be a diagonal matrix, particularly because the
derivative of \(x^{(i)}\) on the output \(y^{(j)}\) is zero for \(i \ne j\).
Thus, we can again use a simple masking, elementwise multiply on the upstream
gradient to compute the local gradient of \(x\) w.r.t. \(y\). In our code we
don’t have to do any “stacking/destacking”; we can actually use the exact same
code mask * y_grad
with both of these being 2D numpy arrays (i.e. matrices)
rather than 1D numpy arrays. The case is similar for larger minibatch sizes
using \(n>2\) samples.
Remark: this process of computing derivatives will be similar to other activation functions because they are elementwise operations.
Affine Layer (Fully Connected), Biases
Now let’s discuss a layer which isn’t elementwise: the fully connected layer operation \(WX+b\). How do we compute gradients? To start, let’s consider one 3D element \(x\) so that our operation is
\[\begin{bmatrix} W_{11} & W_{12} & W_{13} \\ W_{21} & W_{22} & W_{23} \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} + \begin{bmatrix} b_1 \\ b_2 \end{bmatrix} = \begin{bmatrix} y_1 \\ y_2 \end{bmatrix}\]According to the chain rule, the local gradient with respect to \(b\) is
\[\frac{\partial L}{\partial b} = \underbrace{\frac{\partial y}{\partial b}}_{2\times 2} \cdot \underbrace{\frac{\partial L}{\partial y}}_{2\times 1}\]Since we’re doing backpropagation, we can assume the upstream derivative is given, so we only need to compute the \(2\times 2\) Jacobian. To do so, observe that
\[\frac{\partial y_1}{\partial b_1} = \frac{\partial}{\partial b_1} (W_{11}x_1+W_{12}x_2+W_{13}x_3+b_1) = 1\]and a similar case happens for the second component. The offdiagonal terms are zero in the Jacobian since \(b_i\) has no effect on \(y_j\) for \(i\ne j\). Hence, the local derivative is
\[\frac{\partial L}{\partial b} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} \cdot \frac{\partial L}{\partial y} = \frac{\partial L}{\partial y}\]That’s pretty nice — all we need to do is copy the upstream derivative. No additional work necessary!
Now let’s get more realistic. How do we extend this when \(X\) is a matrix? Let’s continue the same notation as we did in the ReLU case, so that our columns are \(x^{(i)}\) for \(i=\{1,2,\ldots,n\}\). Thus, we have:
\[\begin{bmatrix} W_{11} & W_{12} & W_{13} \\ W_{21} & W_{22} & W_{23} \end{bmatrix} \begin{bmatrix} x_1^{(1)} & \cdots & x_1^{(n)} \\ x_2^{(1)} & \cdots & x_2^{(n)} \\ x_3^{(1)} & \cdots & x_3^{(n)} \end{bmatrix} + \begin{bmatrix} b_1 & \cdots & b_1 \\ b_2 & \cdots & b_2 \end{bmatrix} = \begin{bmatrix} y_1^{(1)} & \cdots & y_1^{(n)} \\ y_2^{(1)} & \cdots & y_2^{(n)} \end{bmatrix}\]Remark: crucially, notice that the elements of \(b\) are repeated across columns.
How do we compute the local derivative? We can try writing out the derivative rule as we did before:
\[\frac{\partial L}{\partial b} = \frac{\partial y}{\partial b} \cdot \frac{\partial L}{\partial y}\]but the problem is that this isn’t matrix multiplication. Here, \(y\) is a function from \(\mathbb{R}^2\) to \(\mathbb{R}^{2\times n}\), and to evaluate the derivative, it seems like we would need a 3D matrix for full generality.
Fortunately, there’s an easier way with computational graphs. If you draw out the computational graph and create nodes for \(Wx^{(1)}, \ldots, Wx^{(n)}\), you see that you have to write \(n\) plus nodes to get the output, each of which takes in one of these \(Wx^{(i)}\) terms along with adding \(b\). Then this produces \(y^{(i)}\). See my handdrawn diagram:
This captures the key property of independence among the samples in \(X\). To compute the local gradients for \(b\), it therefore suffices to compute the local gradients for each of the \(y^{(i)}\) and then add them together. (The rule in computational graphs is to add incoming derivatives, which can be verified by looking at trivial 1D examples.) The gradient is
\[\frac{\partial L}{\partial b} = \sum_{i=1}^n \frac{\partial y^{(i)}}{\partial b} \frac{\partial L}{\partial y^{(i)}} = \sum_{i=1}^n \frac{\partial L}{\partial y^{(i)}}\]See what happened? This immediately reduced to the same case we had earlier,
with a \(2\times 2\) Jacobian being multiplied by a \(2\times 1\) upstream
derivative. All of the Jacobians turn out to be the identity, meaning that the
final derivative \(\frac{\partial L}{\partial b}\) is the sum of the columns of
the original upstream derivative matrix \(Y\). As a sanity check, this is a
\((2\times 1)\)dimensional vector, as desired. In numpy, one can do this with
something similar to np.sum(Y_grad)
, though you’ll probably need the axis
argument to make sure the sum is across the appropriate dimension.
Affine Layer (Fully Connected), Weight Matrix
Going from biases, which are represented by vectors, to weights, which are represented by matrices, brings some extra difficulty due to that extra dimension.
Let’s focus on the case with one sample \(x^{(1)}\). For the derivative with respect to \(W\), we can ignore \(b\) since the multivariate chain rule states that the expression \(y^{(1)}=Wx^{(1)}+b\) differentiated with respect to \(W\) causes \(b\) to disappear, just like in the scalar case.
The harder part is dealing with the chain rule for the \(Wx^{(1)}\) expression, because we can’t write the expression “\(\frac{\partial}{\partial W} Wx^{(1)}\)”. The function \(Wx^{(1)}\) is a vector, and the variable we’re differentiating here is a matrix. Thus, we’d again need a 3D like matrix to contain the derivatives.
Fortunately, there’s an easier way with the chain rule. We can still use the rule, except we have to sum over the intermediate components, as specified by the chain rule for higher dimensions; see the Wikipedia article for more details and justification. Our “intermediate component” here is the \(y^{(1)}\) vector, which has two components. We therefore have:
\[\begin{align} \frac{\partial L}{\partial W} &= \sum_{i=1}^2 \frac{\partial L}{\partial y_i^{(1)}} \frac{\partial y_i^{(1)}}{\partial W} \\ &= \frac{\partial L}{\partial y_1^{(1)}}\begin{bmatrix}x_1^{(1)}&x_2^{(1)}&x_3^{(1)}\\0&0&0\end{bmatrix} + \frac{\partial L}{\partial y_2^{(1)}}\begin{bmatrix}0&0&0\\x_1^{(1)}&x_2^{(1)}&x_3^{(1)}\end{bmatrix} \\ &= \begin{bmatrix} \frac{\partial L}{\partial y_1^{(1)}}x_1^{(1)} & \frac{\partial L}{\partial y_1^{(1)}} x_2^{(1)}& \frac{\partial L}{\partial y_1^{(1)}} x_3^{(1)}\\ \frac{\partial L}{\partial y_2^{(1)}} x_1^{(1)}& \frac{\partial L}{\partial y_2^{(1)}} x_2^{(1)}& \frac{\partial L}{\partial y_2^{(1)}} x_3^{(1)}\end{bmatrix} \\ &= \begin{bmatrix} \frac{\partial L}{\partial y_1^{(1)}} \\ \frac{\partial L}{\partial y_2^{(1)}}\end{bmatrix} \begin{bmatrix} x_1^{(1)} & x_2^{(1)} & x_3^{(1)}\end{bmatrix}. \end{align}\]We fortunately see that it simplifies to a simple matrix product! This seems to suggest the following rule: try to simplify any expressions to straightforward Jacobians, gradients, or scalar derivatives, and sum over as needed. Above, splitting the components of \(y^{(1)}\) allowed us to utilize the derivative \(\frac{\partial y_i^{(1)}}{\partial W}\) since \(y_i^{(1)}\) is now a realvalued function, thus enabling straightforward gradient derivations. It also meant the upstream derivative could be analyzed componentbycomponent, making our lives easier.
A similar case holds for when we have multiple columns \(x^{(i)}\) in \(X\). We would have another sum above, over the columns, but fortunately this can be rewritten as matrix multiplication.
Convolutional Layers
How do we compute the convolutional layer gradients? That’s pretty complicated so I’ll leave that as an exercise for the reader. For now.

In fact, \(X\) is in general a tensor. Sophisticated software packages will generalize \(X\) to be tensors. For example, we need to add another dimension to \(X\) with image data since we’ll be using, say, \(28\times 28\) data instead of \(28\times 1\) data (or \(3\times 1\) data in my trivial example here). However, for the sake of simplicity and intuition, I will deal with simple column vectors as samples within a matrix \(X\). ↩
Keeper of the Olympic Flame: Lake Placid’s Jack Shea vs. Avery Brundage and the Nazi Olympics (Story of My GreatUncle)
I just read Keeper of the Olympic Flame: Lake Placid’s Jack Shea vs. Avery Brundage and the Nazi Olympics. This is the story of Jack Shea, a speedskater from Lake Placid, NY, who won two gold medals in the 1932 Winter Olympics (coincidentally, also in Lake Placid). Shea became a local hometown hero and helped to put Lake Placid on the map.
Then, a few years later, Jack Shea boycotted the 1936 Winter Olympics since they were held in Nazi Germany. Jack Shea believed – rightfully – that any regime that discriminated against Jews to the extent the Nazis did had no right to host such an event. Unfortunately, the man in charge of the decision, Avery Brundage, had the last call and decided to include Americans in the Olympics. (Due to World War II, The Winter Olympics would not be held again until 1948.) The book discusses Shea’s boycott – including the striking letter he wrote to Brundage – and then moves on to the 1980 Winter Olympics, which also was held in Lake Placid.
I enjoyed reading Keeper of the Olympic Flame to learn more about the history of the Winter Olympics and the intersection of athletics and politics.
The book also means a lot to me because Jack Shea was my greatuncle. For me, the pictures and stories within it are riveting. As I read the book, I often wondered about what life must have been like in those days, particularly for my distant relatives and ancestors.
I only met Jack Shea once, at a funeral for his sister (my greataunt). Jack Shea died in a car accident in 2002 at the age of 91, presumably from a 36yearold drunk motorist who escaped prosecution. This would be just weeks before his grandson, Jimmy Shea, won a gold meal in skeleton during the 2002 Salt Lake City Winter Olympics. I still remember watching Jimmy win the gold medal and showing everyone his picture of Jack Shea in his helmet. Later, I would personally meet Jimmy and other relatives in a postOlympics celebration.
I wish I had known Jack Shea better, as he seemed like a highcharacter individual. I am glad that this book is here to partially make up for that.
The End of Identity Politics?
On November 18, 2016, there was a fantastic essay in the New York Times by Mark Lilla of Columbia University called “The End of Identity Liberalism”. This essay will go down in history as one that I will remember for a long time.
I have long wanted to write something about identity politics, but I could never find the time to research and eloquently describe my beliefs on such a sensitive topic, so a concise reaction to Lilla’s essay will have to do for now.
Despite being a registered Democrat, one area where I seem to disagree with liberals — at least if we can infer anything from the 2016 election, which admittedly is asking for a lot — is over the issue of identity politics. I personally feel uncomfortable at best about the practice of identity politics. I also believe that, while identity politics obviously has wellmeaning intentions, it accelerates the development of undesirable side effects.
Exhibit A: the election of Donald Trump (well, undesirable to most liberals).
When I was reading Lilla’s essay, the following passage hit home to me:
[Hillary Clinton] tended on the campaign trail to lose that large vision and slip into the rhetoric of diversity, calling out explicitly to AfricanAmerican, Latino, L.G.B.T. and women voters at every stop. This was a strategic mistake. If you are going to mention groups in America, you had better mention all of them. If you don’t, those left out will notice and feel excluded. Which, as the data show, was exactly what happened with the white working class and those with strong religious convictions. Fully twothirds of white voters without college degrees voted for Donald Trump, as did over 80 percent of white evangelicals.
While it is true that, if I had to pick any race to associate with the “privileged” label, white Americans would be the easy choice. However, as suggested by Joe Biden, it is difficult for the white working class to associate themselves with privilege and with identity liberalism.
Aside from the working class whites, another group of people in America who I believe “lack privilege” are people with disabilities. I also think that this group fails to get sufficient recognition compared to other groups (relative to population size). That is not to say that the group is ignored, but with limited time and money, political parties have to selectively choose what to promote and champion. Clinton talked about supporting disabled people in her campaign, but this was probably more motivated from Trump’s actions than a Democratled initiative to treat disabled people as a political group with higher priority than others. (Again, it’s not about being in favor of or against of, but about the priority level.)
After thinking about it, even though I might benefit from increased “identity politics” towards people with disabilities, I still would probably feel uncomfortable taking part or engaging in the practice, given that any such focus on a group of people necessarily leaves out others.
A second reason why I would feel uncomfortable is that within voting blocks, we are seeing increased diversity. According to exit polls of the 2016 election. Trump actually made gains among African Americans, Hispanic Americans, and Asian Americans compared to Mitt Romney! Sometimes I worry that the focus on labeling groups of people has the effect that Lilla observes later:
The surprisingly high percentage of the Latino vote that went to Mr. Trump should remind us that the longer ethnic groups are here in this country, the more politically diverse they become.
I wouldn’t want my beliefs pigeonholed just because of who I am. (Sadly, I have experienced several frustrating examples of this within the deaf community.)
Overall, I prefer the approach where policies are designed to bring everyone up together, so long as they are given an equal starting ground. I know that this is sadly not true in America yet. Therefore, if I had to support any form of identity politics, it would be one which focuses chiefly on improving the lives of children from lowincome families within the first 510 years of life under the critical junction of education and nutrition.
I am under no illusions that part of why I did well in school is that I didn’t grow up in poverty. And indeed, being 50% Asian and 50% Caucasian might have helped (though Asians are often not treated as minority groups, and mixedrace people are often ignored in polls about race, but those are subjects for another day).
Ultimately, identity politics is probably not going to make or break my life goals at this point. My impetus in raising this discussion, however, is largely about my concern over the future of many students and old friends that I know from high school. Some come from lowincome families and face challenges in their lives which are not prioritized by the current identity liberalism. For instance, I was shocked to learn this year when someone I knew from high school was recently arrested for attempted murder. I still have access to his Facebook page, and it’s heartbreaking to read. His wife posts pictures of him and his child and keeps asking him to “hang in there” until he can return to the family.
This guy is younger than me and his future already seems dashed. His biggest challenge in life is probably that he shares my disability, so I worry that people similar to him will never be able to escape out of their cycle of poverty. I hope that there is a way that we can move towards an identityfree future without the risk of alienating people like him, and also of course, with the effect of increasing the economic possibilities and fairness for all of us.
Some Interesting Artificial Intelligence Research Papers I've Been Reading
One of the advantages of having a fourweek winter break between semesters is that, in addition to reading interesting, challenging nonfiction books, I can also catch up on reading academic research papers. For the last few years, I’ve mostly read research papers by downloading them online, and then reading the PDFs on my laptop while taking notes using the Mac Preview app and its highlighting and notetaking features. For me, research papers are challenging to read, so the notes mean I can write stuff in plain English on the PDF.
However, there are a lot of limitations with the Preview app, so I thought I would try something different: how about making a GitHub repository which contains a list of papers that I have read (or plan to read) and where I can write extensive notes. The repository is now active. Here are some papers I’ve read recently, along with a oneparagraph summary for each of them. All of them are “current,” from 2016.

Stochastic Neural Networks for Hierarchical Reinforcement Learning, arXiv, by Carlos Florensa et al. This paper proposes using Stochastic Neural Networks (SNNs) to learn a variety of lowlevel skills for a reinforcement learning setting, particularly one which has sparse rewards (i.e. usually getting 0s instead of +1s and 1s). One key assumption is that there is a pretraining environment, where the agent can learn skills before being deployed in “official” scenarios. In this environment, SNNs are used along with an informationtheoretic regularizer to ensure that the skills learned are different. For the overall highlevel policy, they use a separate (i.e. not jointly optimized) neural network, trained using Trust Region Policy Optimization. The paper benchmarks on a swimming environment, where the agent exhibits different skills in the form of different swimming directions. This wasn’t what I was thinking of when I thought about skills, though, but I guess it is OK for one paper. It would also help if I were more familiar with SNNs.

#Exploration: A Study of CountBased Exploration for Deep Reinforcement Learning, arXiv, by Haoran Tang et al. I think by countbased reinforcement learning, we refer to algorithms which explicitly keep track of \(N(s,a)\), stateaction visitation counts, and which turn that into an exploration strategy. However, these are only feasible for small, finite MDPs when states will actually be visited more than once. This paper aims to blend countbased reinforcement learning to the highdimensional setting by cleverly applying a hash function to map states to hash codes, which are then explicitly counted. Ideally, states which are similar to each other should have the same or similar hash codes. The paper reports the surprising fact (both to me and to them!) that such countbased RL, with an appropriate hash code of course, can reach near state of the art performance on complicated domains such as Atari 2600 games and continuous problems in the RLLab library.

RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning, arXiv, by Rocky Duan et al. The name of this paper, RL^2, comes from “using reinforcement learning to learn a reinforcement learning algorithm,” specifically, by encoding it inside the weights of a Recurrent Neural Network. The hope is that the RNN can help encode some prior knowledge to accelerate the training for reinforcement learning algorithms, hence “fast” reinforcement learning. The slow part is the process of training the RNN weights, but once this is done, the RNN effectively encodes its own reinforcement learning algorithm! Ideally, learning this way will be faster than DQNbased and policy gradientbased algorithms, which are bruteforce and require lots of samples. One trouble I had when reading this paper was trying to wrap my head around what it means to “learn a RL algorithm” and I will probably inspect the source code if I want to really understand it. Some intuition that might help: experiments are applied on a distribution of MDPs, i.e., randomly sampling closelyrelated MDPs, because the highlevel learning process must optimize over something, hence it optimizes over the MDP distribution.

Deep Visual Foresight for Planning Robot Motion, arXiv, by Chelsea Finn et al. This paper takes an important step in letting robots predict the outcome of their own actions, rather than relying on costly human intervention, such as if a human were to collect data and provide feedback. In addition, models that are handengineered often fail on real, unstructured, openworld problems, so it makes sense to abstract this all away (i.e. pull humans out of the loop) and learn everything. There are two main contributions of the paper. The first is a deep predictive model of videos which, given a current frame and a sequence of future actions, can predict the future sequence of frames. Naturally, it uses LSTMs. (Note that these “frames” are — at least in this paper — images of a set of objects cluttered together.) The second contribution is a Model Predictive Control algorithm which, when provided raw pixels of the environment and the goal, can determine the sequence of actions that maximizes the probability of the designated pixel(s) moving to the goal position(s). The experiments test nonprehensile motion, or motion which does not rely on grasping but pushing objects, and show that their algorithm can successfully learn to do so with minimal human intervention.

Value Iteration Networks, NIPS 2016 (Best Paper Award), by Aviv Tamar et al. This introduces the Value Iteration Network, which is a neural network module that learns how to plan by computing an approximate version of value iteration via convolutional neural networks. I think I understand it: the number of actions is the number of channels in a convolutional layer, which themselves represent \(\bar{Q}(s,a)\) values, so by maxpooling over that channel, we get \(\bar{V}(s)\). VINs are differentiable, so by inserting it inside a larger neural network somewhere, it can be trained using endtoend backpropagation. The motivation for VINs is from an example where they sample MDPs from a distribution of MDPs, specifically for GridWorld scenarios where start/goal states and obstacles are randomly assigned. When training on a fixed GridWorld, a DQN or similar algorithm can learn how to navigate it, but it will be unlikely to generalize on a newly sampled GridWorld. Hence, the agent is not learning how to plan to solve “GridWorldlike” scenarios; it doesn’t understand the goaldirected nature of the problem. More generally, agents need to plan on some model of the domain, rather than a fixed MDP. This is where VINs help, since they enable the entire agent architecture to map from observations to not only actions, but to planning computations. Another contribution of their paper is the idea of using another MDP, which they denote as \(\bar{M}\) along with the subsequent \(\bar{s}, \bar{\pi}\), etc., and then utilizing the solution to that for the original MDP \(M\).

Principled Option Learning in Markov Decision Processes, EWRL 2016, by Roy Fox et al. This paper is about hierarchical reinforcement learning, where the “subpolicies” at the base of the hierarchy are called “options”, and the higherlevel policy is allowed to pick and execute them. This paper assumes a prior policy on an agent, and then uses information theory to develop options. For instance, in a two room domain, if we constrain the number of options to two, then one option should automatically learn to go from left to right, and another should go from right to left. The hardest part about understanding this paper is probably that there is no clear algorithm that connects it with the overall MDP environment. There’s an algorithm there that describes how to learn the options, but I’m still not sure how it works from starttofinish in a clean MDP.

Taming the Noise in Reinforcement Learning via Soft Updates, UAI 2016, by Roy Fox et al. This paper introduces the Glearning algorithm for reinforcement learning, which I’ve correctly implemented (I hope) in my personal reinforcement learning GitHub repository. The central motivation is that the popular Qlearning algorithm learns biased estimates of the stateaction values due to taking the “max” operator (or “min” in the case of costs). Therefore, it makes sense to use “soft updates” where, instead of taking the single best “successor action” as in the standard Qlearning temporal difference update, we use all of the actions, weighted according to probability. This is, I believe, implemented by imposing a prior policy on the agent, and then formulating an appropriate cost function, and then deriving a \(\pi(a's')\) which enforces the “softness.” The Glearning algorithm relies on a tuning parameter which at the start of training values the prior more, and then as time goes on, Glearning gradually shifts to a more deterministic policy. The Glearning update is similar to the Qlearning update, except it uses informationtheory to mitigate the effect of bias. I haven’t been able to do the entire derivation.
Aside from these papers being Artificial Intelligencerelated, one aspect that seems to be common to these papers is the need for either hierarchical learning or increased abstraction. The former can probably be considered a subset of the latter since we “abstract away” lowerlevel policies in lieu of focusing on the higherlevel policy. We can also abstract away the process of choosing MDPs or a pipeline and just learn them (e.g., the Deep Visual Foresight and the RL^2 papers). That might be something useful to keep in mind when doing research in this day and age.