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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Length 826 pages
Edition 2nd Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (28) Chapters Close

Preface 1. What Is Reinforcement Learning? 2. OpenAI Gym FREE CHAPTER 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. Higher-Level RL Libraries 8. DQN Extensions 9. Ways to Speed up RL 10. Stocks Trading Using RL 11. Policy Gradients – an Alternative 12. The Actor-Critic Method 13. Asynchronous Advantage Actor-Critic 14. Training Chatbots with RL 15. The TextWorld Environment 16. Web Navigation 17. Continuous Action Space 18. RL in Robotics 19. Trust Regions – PPO, TRPO, ACKTR, and SAC 20. Black-Box Optimization in RL 21. Advanced Exploration 22. Beyond Model-Free – Imagination 23. AlphaGo Zero 24. RL in Discrete Optimization 25. Multi-agent RL 26. Other Books You May Enjoy
27. Index

The imagination-augmented agent

The overall idea of the new architecture, called imagination-augmented agent (I2A), is to allow the agent to imagine future trajectories using the current observations and incorporate these imagined paths into its decision process. The high-level architecture is shown in the following diagram:

Figure 22.1: The I2A architecture

The agent consists of two different paths used to transform the input observation: model-free and imagination. Model-free is a standard set of convolution layers that transforms the input image in high-level features. The other path, imagination, consists of a set of trajectories imagined from the current observation. The trajectories are called rollouts and they are produced for every available action in the environment. Every rollout consists of a fixed number of steps into the future, and on every step, a special model, called the environment model (EM) (but not to be confused with the expectation maximization method...

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