Reinforcement Learning Foundations

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