Reinforcement Learning 1: Introduction
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
trial-and-error
“Law of Effect”
k-armed bandit
temporal-difference
actor-critic architecture
Q-learning