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Intro
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Classroom Contents
MIT: Reinforcement Learning
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- 1 Intro
- 2 Learning in Dynamic Environments
- 3 Classes of Learning Problems
- 4 Reinforcement Learning (RL): Key Concepts
- 5 Defining the Q-function
- 6 Deep Reinforcement Learning Algorithms
- 7 Digging deeper into the Q-function
- 8 Deep Q Network Summary
- 9 Downsides of Q-learning
- 10 Discrete vs Continuous Action Spaces
- 11 Policy Gradient (PG): Key Idea
- 12 Training Policy Gradients: Case Study
- 13 Reinforcement Learning in Real Life
- 14 Reinforcement Learning and the Game of Go
- 15 Deep Reinforcement Learning Summary
