You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Chapter 13 – TRPO, PPO, and ACKTR Methods

The trust region implies the region where our actual function f(x) and approximated function are close together. So, we can say that our approximation will be accurate if our approximated function is in the trust region.
TRPO is a policy gradient algorithm, and it acts as an improvement to policy gradient with baseline. TRPO tries to make a large policy update while imposing a KL constraint that the old policy and the new policy should not vary from each other too much. TRPO guarantees monotonic policy improvement, guaranteeing that there will always be a policy improvement on every iteration.
Just like gradient descent, conjugate gradient descent also tries to find the minimum of the function; however, the search direction of conjugate gradient descent will be different from gradient descent and conjugate gradient descent attains convergence in N iterations.
The update rule of TRPO is given as .
PPO...