Reinforcement Learning Foundations

Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.

Introduction

• Reinforcement learning in a nutshell

1. Getting Started with Reinforcement Learning

• Terms in reinforcement learning
• A basic RL problem
• Markov decision process
• A basic RL solution

2. Reinforcement Learning Algorithms

• Monte Carlo method
• Temporal difference methods
• Other RL algorithms

3. Monte Carlo Method

• The setting
• Exploration and exploitation
• Monte Carlo prediction
• First visit and every visit MC prediction
• Monte Carlo control
• Additional modifications

4. Temporal Difference Methods

• The setting
• SARSA
• SARSAMAX (Q-learning)
• Expected SARSA

5. Modified Forms of Reinforcement

• Deep reinforcement learning
• Multi-agent reinforcement learning
• Inverse reinforcement learning

Conclusion

• Your reinforcement learning journey