This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from
Each folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.
All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.
- 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
- Function Approximation
- Deep Q Learning (WIP)
- Policy Gradient Methods (WIP)
- Learning and Planning (WIP)
- Exploration and Exploitation (WIP)
- [Dynamic Programming Policy Evaluation](DP/Policy Evaluation Solution.ipynb)
- [Dynamic Programming Policy Iteration](DP/Policy Iteration Solution.ipynb)
- [Dynamic Programming Value Iteration](DP/Value Iteration Solution.ipynb)
- [Monte Carlo Prediction](MC/MC Prediction Solution.ipynb)
- [Monte Carlo Control with Epsilon-Greedy Policies](MC/MC Control with Epsilon-Greedy Policies Solution.ipynb)
- [Monte Carlo Off-Policy Control with Importance Sampling](MC/Off-Policy MC Control with Weighted Importance Sampling Solution.ipynb)
- [SARSA (On Policy TD Learning)](TD/SARSA Solution.ipynb)
- [Q-Learning (Off Policy TD Learning)](TD/Q-Learning Solution.ipynb)
- [Q-Learning with Linear Function Approximation](FA/Q-Learning with Value Function Approximation Solution.ipynb)
- [Deep Q-Learning for Atari Games](DQN/Deep Q Learning Solution.ipynb)
- [Double Deep-Q Learning for Atari Games](DQN/Double DQN Solution.ipynb)
- Deep Q-Learning with Prioritized Experience Replay (WIP)
- [Policy Gradient: REINFORCE with Baseline](PolicyGradient/CliffWalk REINFORCE with Baseline Solution.ipynb)
- [Policy Gradient: Actor Critic with Baseline](PolicyGradient/CliffWalk Actor Critic Solution.ipynb)
- [Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces](PolicyGradient/Continuous MountainCar Actor Critic Solution.ipynb)
- Deterministic Policy Gradients for Continuous Action Spaces (WIP)
- Deep Deterministic Policy Gradients (DDPG) (WIP)
- Asynchronous Advantage Actor Critic (A3C)
Textbooks:
Classes:
- David Silver's Reinforcement Learning Course (UCL, 2015)
- CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)
- CS 8803 - Reinforcement Learning (Georgia Tech)
- [Deep Reinforcement Learning and Control ] (https://katefvision.github.io/)
Talks/Tutorials:
- Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- Deep Reinforcement Learning Slides @ NIPS 2016
Other Projects:
Selected Papers:
- Human-Level Control through Deep Reinforcement Learning (2015-02)
- Deep Reinforcement Learning with Double Q-learning (2015-09)
- Continuous control with deep reinforcement learning (2015-09)
- Prioritized Experience Replay (2015-11)
- Dueling Network Architectures for Deep Reinforcement Learning (2015-11)
- Asynchronous Methods for Deep Reinforcement Learning (2016-02)
- Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016-03)
- Mastering the game of Go with deep neural networks and tree search