Skip all the talk and go directly to the Github Repo with code and exercises.
Why Study Reinforcement Learning
Reinforcement Learning is one of the fields I’m most excited about. Over the past few years amazing results like learning to play Atari Games from raw pixelsand Mastering the Game of Gohave gotten a lot of attention, but RL is also widely used in Robotics, Image Processing and Natural Language Processing.
Combining Reinforcement Learning and Deep Learning techniques works extremely well. Both fields heavily influence each other. On the Reinforcement Learning side Deep Neural Networks are used as function approximators to learn good representations, e.g. to process Atari game images or to understand the board state of Go. In the other direction, RL techniques are making their way into supervised problems usually tackled by Deep Learning. For example, RL techniques are used to implement attention mechanisms in image processing, or to optimize long-term rewards in conversational interfaces and neural translation systems. Finally, as Reinforcement Learning is concerned with making optimal decisions it has some extremely interesting parallels to human Psychology and Neuroscience (and many other fields).
With lots of open problems and opportunities for fundamental research I think we’ll be seeing multiple Reinforcement Learning breakthroughs in the coming years. And what could be more fun than teaching machines to play Starcraft and Doom?
How to Study Reinforcement Learning
There are many excellent Reinforcement Learning resources out there. Two I recommend the most are:
David Silver’s Reinforcement Learning Course
Richard Sutton’s & Andrew Barto’s Reinforcement Learning: An Introduction (2nd Edition)book.
The latter is still work in progress but it’s ~80% complete. The course is based on the book so the two work quite well together. In fact, these two cover almost everything you need to know to understand most of the recent research papers. The prerequisites are basic Math and some knowledge of Machine Learning.
That covers the theory. But what about practical resources? What about actually implementing the algorithms that are covered in the book/course? That’s where this post and the Github repositorycomes in. I’ve tried to implement most of the standard Reinforcement Algorithms using Python, OpenAI Gymand Tensorflow. I separated them into chapters (with brief summaries) and exercises and solutions so that you can use them to supplement the theoretical material above. All of this is in the Github repository.
Some of the more time-intensive algorithms are still work in progress, so feel free to contribute. I’ll update this post as I implement them.
Table of Contents
Introduction to RL problems, OpenAI gym
MDPs and Bellman Equations
Dynamic Programming: Model-Based RL, Policy Iteration and Value Iteration
Monte Carlo Model-Free Prediction & Control
Temporal Difference Model-Free Prediction & Control
Deep Q Learning(WIP)
Policy Gradient Methods(WIP)
Learning and Planning (WIP)
Exploration and Exploitation (WIP)
List of Implemented Algorithms
Dynamic Programming Policy Evaluation
Dynamic Programming Policy Iteration
Dynamic Programming Value Iteration
Monte Carlo Prediction
Monte Carlo Control with Epsilon-Greedy Policies
Monte Carlo Off-Policy Control with Importance Sampling
SARSA (On Policy TD Learning)
Q-Learning (Off Policy TD Learning)
Q-Learning with Linear Function Approximation
Deep Q-Learning for Atari Games
Double Deep-Q Learning for Atari Games
Deep Q-Learning with Prioritized Experience Replay (WIP)
Policy Gradient: REINFORCE with Baseline
Policy Gradient: Actor Critic with Baseline
Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces
Deterministic Policy Gradients for Continuous Action Spaces (WIP)