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This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).
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Actions
Agents
Diagnostics
Environments
Feature Extractors
Rewards
States
Training and Running Agents
Value Estimation
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Chapter 1: Introduction
Chapter 2: Multi-armed Bandits
Chapter 3: Finite Markov Decision Processes
Chapter 4: Dynamic Programming
Chapter 5: Monte Carlo Methods
Chapter 6: Temporal-Difference Learning
Chapter 8: Planning and Learning with Tabular Methods
Chapter 9: On-policy Prediction with Approximation
Chapter 10: On-policy Control with Approximation
Chapter 13: Policy Gradient Methods