MIT: Reinforcement Learning

MIT: Reinforcement Learning

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Deep Q Network Summary

8 of 15

8 of 15

Deep Q Network Summary

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MIT: Reinforcement Learning

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

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