1 Introduction

1.1 Reinforcement Learning

1.2 Examples

1.3 Elements of Reinforcement Learning

a policy, a reward signal , a value function and, optionally, a model of the environment

model-based or model-free.

1.4 Limitations and Scope

1.5 An Extended Example: Tic-Tac-Toe

1.6 Summary

1.7 Early History of Reinforcement Learning

Markovian decision processes(MDPs)

Bellman equations

dynamic programming

Monte Carlo methods


“Law of Effect”

k-armed bandit


actor-critic architecture