Sampling From a Stochastic Policy

Q-Table Initialization and Indexing

States, Actions, and Rewards Defined

Policy: Mapping States to Actions

States, Actions, and Rewards Defined

Agent, Environment, and Interaction Loop

Real-World Applications of RL

Reward Shaping and Reward Design

The Credit Assignment Problem

Episodic vs Continuing Tasks

On-Policy vs Off-Policy Learning

Temporal Difference Learning Concept

Q-Learning as a Basic RL Algorithm

Model-Based vs Model-Free RL

Markov Decision Process: The RL Framework

Epsilon-Greedy Action Selection

Exploration vs Exploitation Tradeoff

Value Functions: State and Action (Q)

Discount Factor in Reinforcement Learning

Reward Signal vs Long-Term Return