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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

Inverse reinforcement learning

So, what did we do in RL? We tried to find the optimal policy given the reward function. Inverse reinforcement learning is just the inverse of reinforcement learning, that is, the optimal policy is given and we need to find the reward function. But why is inverse reinforcement learning helpful? Designing a reward function is not a simple task and a poor reward function will lead to the bad behavior of an agent. We do not always know the proper reward function but we know the right policy, that is, the right action in each state. So this optimal policy is fed to the agent by human experts and the agents try to learn the reward function. For example, consider an agent learning to walk in a real-world environment; it is difficult to design the reward function for all the actions it will do. Instead, we can feed in to the agents the demonstrations (optimal...

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