Reinforcement Learning

This lecture introduces deep reinforcement learning through Q functions, Deep Q Networks, policy learning algorithms, and policy gradients. It also examines reinforcement learning applications including the VISTA simulator and AlphaGo, AlphaZero, and MuZero.

Syllabus

- Introduction
- Classes of learning problems
- Definitions
- The Q function
- Deeper into the Q function
- Deep Q Networks
- Atari results and limitations
- Policy learning algorithms
- Discrete vs continuous actions
- Training policy gradients
- RL in real life
- VISTA simulator
- AlphaGo and AlphaZero and MuZero
- Summary