Learning Resources
In addition to the documentation provided in this project, there are many valuable resources available for those who wish to deepen their understanding of reinforcement learning and related topics. We have compiled a list of recommended papers, textbooks, and blog posts that we have found particularly helpful. These resources range from beginner-friendly introductions to more advanced topics, and we hope that they will be useful to you in your learning journey. In no particular order, we liked the following resources:
Sutton & Barto, RL Textbook Bible
Implicit Quantile Networks for Distributional Reinforcememnt Learning
Rainbow: Combining Improvements in Deep Reinforcement Learning
While less central to this project, we also liked:
Trackmania - The History of Machine Learning in Trackmania, A really cool blog post
The Phenomenon of Policy Churn, Intuitions about established knowledge can be very very wrong
Return-based Scaling: Yet Another Normalisation Trick for Deep RL, An actual formalization of a recommended practice
Bigger, Better, Faster: Human-level Atari with human-level efficiency, Cool tricks that don’t seem to help, but cool anyway.
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning, same as above
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels, same as above
Understanding Plasticity in Neural Networks, The reason why we train from scratch on a new map… But we haven’t tested much of this.