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Deep Reinforcement Learning with Python

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

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781839210686
Length 760 pages
Edition 2nd Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (22) Chapters Close

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

Prediction and control tasks

In reinforcement learning, we perform two important tasks, and they are:

  • The prediction task
  • The control task

Prediction task

In the prediction task, a policy is given as an input and we try to predict the value function or Q function using the given policy. But what is the use of doing this? Our goal is to evaluate the given policy. That is, we need to determine whether the given policy is good or bad. How can we determine that? If the agent obtains a good return using the given policy then we can say that our policy is good. Thus, to evaluate the given policy, we need to understand what the return the agent would obtain if it uses the given policy. To obtain the return, we predict the value function or Q function using the given policy.

That is, we learned that the value function or value of a state denotes the expected return an agent would obtain starting from that state following some policy . Thus, by predicting...

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