Reinforcement Learning - An Introduction

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Part II: Approximate Solution Methods

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  • Chapter 9: On-policy Prediction with Approximation
  • Chapter 10: On-policy Control with Approximation
  • Chapter 11: Off-policy Methods with Approximation
  • Chapter 12: Eligibility Traces
  • Chapter 13: Policy Gradient Methods

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  • Reinforcement Learning: An Introduction book
  • Part I: Tabular Solution Methods
    • Chapter 2: Multi-armed Bandits
    • Chapter 3: Finite Markov Decision Processes
    • Chapter 4: Dynamic Programming
    • Chapter 5: Monte Carlo Methods
    • Chapter 6: Temporal-Difference Learning
    • Chapter 7: n-step Bootstrapping
    • Chapter 8: Planning and Learning with Tabular Methods
  • Part II: Approximate Solution Methods
    • Chapter 9: On-policy Prediction with Approximation
    • Chapter 10: On-policy Control with Approximation
    • Chapter 12: Eligibility Traces
    • Chapter 13: Policy Gradient Methods
  • Part III: Looking Deeper
    • Chapter 14: Psychology
  • Appendix
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