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Reinforcement Learning with TensorFlow

You're reading from   Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Sayon Dutta Sayon Dutta
Author Profile Icon Sayon Dutta
Sayon Dutta
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Table of Contents (17) Chapters Close

Preface 1. Deep Learning – Architectures and Frameworks 2. Training Reinforcement Learning Agents Using OpenAI Gym FREE CHAPTER 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 15. Further topics in Reinforcement Learning 16. Other Books You May Enjoy

Policy objective functions

Let's discuss now how to optimize a policy. In policy methods, our main objective is that a given policy  with parameter vector  finds the best values of the parameter vector. In order to measure which is the best, we measure  the quality of the policy  for different values of the parameter vector .

Before discussing the optimization methods, let's first figure out the different ways to measure the quality of a policy :

  • If it's an episodic environment,  can be the value function of the start state  that is if it starts from any state , then the value function of it would be the expected sum of reward from that state onwards. Therefore,
  • If it's a continuing environment,  can be the average value function of the states. So, if the environment goes on and...
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