andersy005/reinforcement-learning-an-introduction

Implementation of Reinforcement Learning Algorithms: Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book & David Silver's course.

Reinforcement-Learning: An introduction

Overview

This repository contains code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from:

• Reinforcement Learning: An Introduction (2nd Edition)
• David Silver's Reinforcement Learning Course

Environment and Libraries

• Python3
• Jupyter notebook
• Numpy
• Matplotlib
• Six
• Seaborn

All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.

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TODO

RL Course by David Silver

1. Lectures

• Lecture 1: Introduction to Reinforcement Learning
• ecture 2: Markov Decision Processes
• Lecture 3: Planning by Dynamic Programming
• Lecture 4: Model-Free Prediction
• Lecture 5: Model-Free Control
• Lecture 6: Value Function Approximation
• Lecture 7: Policy Gradient Methods
• Lecture 8: Integrating Learning and Planning
• Lecture 9: Exploration and Exploitation
• Lecture 10: Case Study: RL in Classic Games

2. Reinforcement Learning Assignment: Easy21

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Reinforcement Learning: An Introduction

Notebooks for Sutton & Barto's book Reinforcement Learning: An Introduction (2nd Edition)

Contents

Chapter 1: The Reinforcement Learning Problem

Chapter 2: Multi-arm Bandits

Chapter 3: Finite Markov Decision Processes

Chapter 4: Dynamic Programming

Chapter 5: Monte Carlo Methods

Chapter 6: Temporal-Difference Learning

Chapter 7: Multi-step Bootstrapping

Chapter 8: Planning and Learning with Tabular Methods

Chapter 9: On-policy Prediction with Approximation

Chapter 10: On-policy Control with Approximation

Chapter 11: Off-policy Methods with Approximation

Chapter 12: Eligibility Traces

Chapter 13: Policy Gradient Methods

Extra