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Practical Reinforcement Learning: From Algorithms to Applications

  • Why This Book?

Modeling

  • Why Build a Model? For Whom?
  • The State‑Space Perspective
  • Programs as Models

Optimization-Based Decision Making

  • Discrete-Time Trajectory Optimization
  • Continuous-Time Trajectory Optimization
  • From Trajectories to Policies
  • Dynamic Programming

Learning from Data

  • Approximate Dynamic Programming
  • Policy Parametrization Methods

References

  • Bibliography
  • Repository
  • Open issue

Index

By Pierre-Luc Bacon

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