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Practical Reinforcement Learning: From Algorithms to Applications

  • Why This Book?

Modeling

  • Why Build a Model? For Whom?
  • Dynamics Models for Decision Making
  • Programs as Models

Numerical Trajectory Optimization

  • Discrete-Time Trajectory Optimization
  • Trajectory Optimization in Continuous Time

From Trajectories to Policies

  • Model Predictive Control
  • Dynamic Programming

Approximate Dynamic Programming

  • Smooth Bellman Optimality Equations
  • Projection Methods for Functional Equations
  • Simulation-Based Approximate Dynamic Programming
  • Policy Parametrization Methods

Appendix

  • Example COCPs
  • Solving Initial Value Problems
  • Nonlinear Programming

References

  • Bibliography
  • Repository
  • Open issue

Index

By Pierre-Luc Bacon

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