Reinforcement Learning

Explore deep reinforcement learning in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into key concepts including classes of learning problems, Q functions, deep Q networks, policy learning algorithms, and the distinction between discrete and continuous actions. Examine real-world applications of reinforcement learning, including the VISTA simulator and the groundbreaking AlphaGo and AlphaZero systems. Gain insights into Atari game results, limitations of current approaches, and the challenges of implementing reinforcement learning in real-life scenarios. This 44-minute talk, delivered by Alexander Amini, provides a thorough overview of reinforcement learning techniques and their practical implications in the field of artificial intelligence.

- 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
- Summary