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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

RL glossary

We have learned several important and fundamental concepts of RL. In this section, we revisit several important terms that are very useful for understanding the upcoming chapters.

Agent: The agent is the software program that learns to make intelligent decisions, such as a software program that plays chess intelligently.

Environment: The environment is the world of the agent. If we continue with the chess example, a chessboard is the environment where the agent plays chess.

State: A state is a position or a moment in the environment that the agent can be in. For example, all the positions on the chessboard are called states.

Action: The agent interacts with the environment by performing an action and moves from one state to another, for example, moves made by chessmen are actions.

Reward: A reward is a numerical value that the agent receives based on its action. Consider a reward as a point. For instance, an agent receives +1 point (reward) for a good...

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