reinforcement_learning_an_introduction

Summary (in Korean) and python implementation of 'Reinforcement Learning: An Introduction' written by Sutton & Barto.

1. Introduction

• 1.1 Reinforcement Learning
• 1.2 Examples
• 1.3 Elements of Reinforcement Learning
• 1.4 Limitations and Scope
• 1.5 An Extended Example: Tic-Tac-Toe

2. Multi-armed bandits

• 2.1 k-armed bandit problem
• 2.2 Action-value Methods
• 2.3 The 10-armed Testbed
• 2.4 Incremental Implementation
• 2.5 Tracking a Nonstationary Problem
• 2.6 Optimistic Initial Values
• 2.7 Upper-Confidence-Bound Action Selection
• 2.8 Gradient Bandit Algorithms
• 2.9 Associative Search (Contextual Bandits)

3. Finite Markov Decision Processes

• 3.1 The Agent-Environment Interface
• 3.2 Goals and Rewards
• 3.3 Returns and Episodes
• 3.4 Unified Notation for Episodic and Continuing Tasks
• 3.5 Policies and Value Functions
• 3.6 Optimal Policies and Optimal Value Functions
• 3.7 Optimality and Approximation