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Hands-On Reinforcement Learning with Python

You're reading from   Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Length 318 pages
Edition 1st 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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Table of Contents (16) Chapters Close

Preface 1. Introduction to Reinforcement Learning FREE CHAPTER 2. Getting Started with OpenAI and TensorFlow 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Deep Learning Fundamentals 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Capstone Project – Car Racing Using DQN 13. Recent Advancements and Next Steps 14. Assessments 15. Other Books You May Enjoy

Imagination augmented agents

Are you a fan of the game chess? If I asked you to play chess, how would you play the game? Before moving any pieces on the chessboard, you might imagine the consequences of moving any piece and move the piece you think would help you to win. So, basically, before taking any action, you imagine the consequence and, if it is favorable, you proceed with that action, or else you refrain from performing that action.

Similarly, imagination augmented agents are augmented with imagination; before taking any action in an environment they imagine the consequences of taking the action and, if they think the action will provide a good reward, they will perform the action. They also imagine the consequences of taking a different action. Augmenting agents with imaginations is the next big step towards general artificial intelligence.

Now we will see how imagination...

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