Introduction to Reinforcement Learning

Kursinhalt

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

1. RL Core Theory

What is RL?RL vs Other Learning Paradigms Markov Decision Process Episodes and Returns Model, Policy, and Values Exploration vs Exploitation Gymnasium Basics Challenge: Setting Up an Environment

2. Multi-Armed Bandit Problem

Problem Introduction Action Values Epsilon-Greedy Algorithm Upper Confidence Bound Algorithm Gradient Bandits Algorithm Challenge: Multi-Armed Bandits

3. Dynamic Programming

What is Dynamic Programming?Bellman Equations Optimality Conditions Policy Evaluation Policy Improvement Generalized Policy Iteration Policy Iteration Value Iteration Challenge: Dynamic Programming

4. Monte Carlo Methods

What are Monte Carlo Methods?Value Function Estimation Monte Carlo Control Exploration Approaches On-Policy Monte Carlo Control Off-Policy Monte Carlo Control Incremental Implementations Challenge: Monte Carlo Methods

5. Temporal Difference Learning

What is Temporal Difference Learning?TD(0): Value Function Estimation SARSA: On-Policy TD Learning Q-Learning: Off-Policy TD Learning Generalization of TD Learning

Challenge: Dynamic Programming

Challenge

In this challenge, you will write your own implementations of policy iteration and value iteration, visually observe the agent's behavior, and analyze value function and policy plots.

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 9

some-alt