Review of Deep Reinforcement Learning (CS 294112) at Berkeley
Update October 31, 2016: I received an announcement that CS 294112 will be taught again next semester! That sounds exciting, and while I won’t be enrolling in the course, I will be following its progress and staying in touch on the concepts taught.
And by the way, today I finally published my reinforcement learning post that I said I would write in my July update. You can see it here.
Update July 18, 2016: This post seems to have gotten a considerable amount of attention, at least compared to my normal blog posts, so I would like to answer some of the common questions that I’ve received in either the comments or by private email.

If you’re looking for homework assignments, I first want to warn you that, as I emphasize in the my review, the assignments are probably not going to be as educational as you would want them to be. If you’re still interested, our TA for the class posted a github repository on the Berkeley RLL page with the homework for this class. The homeworks are iPython notebooks (now called Jupyter notebooks, I think). If there’s code in the “YOUR CODE HERE” sections, then you’re probably reading the solutions; I’m not sure if there’s a clean version of the assignments there.

Unfortunately, we did not have any video lectures, slides, or readings outside of what you can see on the class website. A note for those who are reading the comments after this update: the class website was originally pulled down due to “some tyrants” (according to the course staff), but it’s happily now up.

If you’re looking for other resources to learn about deep reinforcement learning, I have several recommendations. In terms of courses, check out David Silver’s reinforcement learning course and the recent Machine Learning Summer School; the latter had our class instructor as part of the course staff, so the material is probably going to be similar to what we covered. (Coming up in a few weeks is the Deep Learning Summer School, something you might also want to check out.) I have all these courses bookmarked and am trying to carve out some time to read the slides. In terms of code, I would strongly recommend starting with either the deep_q_rl library or OpenAI Gym. The former is a super easytoread Python library that allows you to replicate DeepMind’s results in their 2013 and 2015 papers on Atari games. The latter was recently launched, and I don’t have experience with it, but it sounds really cool as we can compare our reinforcement learning implementations.

This is more of a comment than an answer, but I thought I’d mention it anyway: my blog’s comments are handled by Disqus, and in the moderation panel I can see the emails of the commenters. Thus, there is no need to post your email publicly as I can see it regardless.
Thanks everyone, and that’s all! After this paragraph is the original post as I had written it. But one more thing: after rereading this post, I think I was a little too harsh on the class. Furthermore, even though people have said they liked this post, I don’t think I gave reinforcement learning its due. So to rectify my regrets, I’m planning on launching a new series of deep reinforcement learning posts on this blog, similar to the style of Andrej Karpathy’s excellent blog post. I’ve already written a post on basic reinforcement learning, so I’m hoping to progress towards more advanced topics. My goal is to have the first post up sometime in August. Hopefully those will be a good resource for some enthusiasts out there.
What is this course? At the time I enrolled, it was a new twocredit class called Deep Reinforcement Learning (CS 294112) and taught by Pieter Abbeel’s graduate student, John Schulman. It seemed like a cross between a research seminar and a normal lecture course. The former tend to be one or two credits and are principally about relevant research results; the latter tend to be three or four credits and have lectures, homeworks, exams, and projects.
In AI and robotics, reinforcement learning is a standard way of framing a problem. For example, if a robot needs to learn how to play a game, it must engage in “reinforcement learning” to try out different actions, get rewards, and then modify its policy. The word “deep” refers to how deep neural networks have recently become the workhorse of stateoftheart reinforcement learning. (This is why the class wasn’t taught until now.) The broader category of deep learning involves the use of deep neural networks in other applications, such as image classification and speech recognition. Deep learning has become so popular that Google even paid $400 million to buy a deep learning company, DeepMind.
The class had about eighty students, so to avoid getting into trouble with the building managers about stuffing too many people in one room, John gave two identical lectures for each class day. I remained in the afternoon session to make it easy on the interpreters’ schedules, but unfortunately, most of the other students picked the afternoon session, but hey, they don’t have my excuse … perhaps they can’t wake up early? So once again, a graduate level class had some of its students sitting on the floor. Seems like that’s a common problem here, huh?
Anyway, back to the class discussion. The first few lectures were about Markov Decision Processes and neural networks, so if there were any classes to miss, it would be those because I already knew the material.
The remaining lectures were, to be frank, difficult, and I often felt mentally stressed in class. Most of the content was pure math, and the derivations were a long sequence of sums, expectations, and other terms, each of which were more sums and expectations. For instance, look at the formula for policy gradients:
\[g = \mathbb{E}\left[\sum_{t=1}^T \Psi_t \nabla_\theta \log \pi_\theta (a_t \mid s_t)\right]\]To understand^{1} this, one has to process lots of material, such as what it means to take the gradient of the log of a policy, and that \(\Psi_t\) isn’t just a simple scalar but can represent concepts like the advantage function, which involves another sequence of expectations and sums of rewards. Connecting this material is challenging in real time, and I felt that the lectures did not provide sufficient intuition. My sign language interpreters tried to repeat the exact words John uttered, but despite this, I could not translate this process into clear mathematical comprehension.
Given that the lectures were difficult for me to follow, I hoped that homeworks would be more useful. The homeworks in this class were provided as IPython/Jupyter notebooks. We had starter code and needed to fill in the “YOUR CODE HERE” sections.
The first homework was nearly trivial for people who knew about the basics of Markov Decision Process, Value Iteration, Policy Iteration, and QLearning. I wrote about thirty lines of Python code for the entire assignment.
The second homework, on policy gradients, was more interesting, but the release date kept getting postponed. It soon became a running joke in class whenever John said: “Oh, and about that second homework, we plan to release it in a few days…”. It was finally released on October 11. (John on Piazza: “You may have given up hope that this day would ever come, but behold, HW2 is finally here.”) To put this in perspective, the first homework assignment was due on September 7.
Fortunately, the second assignment was more challenging than the first, and I had to be careful in implementing formulas since math from research papers doesn’t always translate neatly into code. I was pleased to see that the homework was designed so naive implementations of formulas would take too long to test. (I believe AI assignments should require code to be reasonably optimized.)
We were going to have homeworks on approximate dynamic programming and supervised learning, but since the second homework got delayed so much and the third one would have taken too long to create, the staff canceled all future assignments.
To be honest, the main deep reinforcement learning material I learned this semester didn’t even come from this class. In Pieter’s Advanced Robotics (CS 287) class, which I also took this semester, my final project was about deep learning for Atari games. I had time to sufficiently read and absorb the Atari deep learning research papers, which helped me to better understand some of the material in this class (CS 294112). Consequently, my recommendation for someone who wants to take this class in the future is to, if possible, take CS 287 concurrently and do a project that uses neural networks. That way, one gets to do deep reinforcement learning.
To recap, here are some of the positive aspects of the class:
 It covers a popular and interesting research area.
 It presents many relevant research papers, including those from Berkeley students.
 For a class that is almost like a research seminar, there are many online resources one can consult for additional background. Unfortunately, a lot of the written references are also hard to understand.
 It is easy to obtain homework help on Piazza.
Here are some of the negative ones:
 The lectures were not polished and involved lots of math without intuition. This issue is understandable because it was a first time course taught by a graduate student.
 There did not seem to be much advance preparation for the course in terms of lecture material. The course website had a brief outline of lectures, but we had to change some of that on short notice.
 It did not provide sufficiently many or sufficiently difficult homework assignments. Having more indepth assignments would let me deeply reinforce my understanding (pun fully intended).
Ultimately, this course allowed me to scratch the surface of deep reinforcement learning, though it was immensely frustrating for me to try and understand the material directly from the lecture, and the haphazard nature of the course did not help. I suspect that future iterations of the course will proceed more smoothly, and yes, even though no one’s told me personally, this class will be offered again (in some form) so long as deep learning remains the king of machine learning.

Update November 3, 2016: After studying more about policy gradients, I now feel like I truly understand this formula. ↩