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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

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By Pierre-Luc Bacon

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