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.
Overview
Syllabus
Introduction
- Reinforcement learning in a nutshell
- Terms in reinforcement learning
- A basic RL problem
- Markov decision process
- A basic RL solution
- Monte Carlo method
- Temporal difference methods
- Other RL algorithms
- The setting
- Exploration and exploitation
- Monte Carlo prediction
- First visit and every visit MC prediction
- Monte Carlo control
- Additional modifications
- The setting
- SARSA
- SARSAMAX (Q-learning)
- Expected SARSA
- Deep reinforcement learning
- Multi-agent reinforcement learning
- Inverse reinforcement learning
- Your reinforcement learning journey
Taught by
Khaulat Abdulhakeem
