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Hands-On Reinforcement Learning with Python

You're reading from  Hands-On Reinforcement Learning with Python

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Sudharsan Ravichandiran Sudharsan Ravichandiran
Profile icon Sudharsan Ravichandiran

Table of Contents (16) Chapters

Preface 1. Introduction to Reinforcement Learning 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

Policy gradient

The policy gradient is one of the amazing algorithms in reinforcement learning (RL) where we directly optimize the policy parameterized by some parameter . So far, we have used the Q function for finding the optimal policy. Now we will see how to find the optimal policy without the Q function. First, let's define the policy function as , that is, the probability of taking an action a given the state s. We parameterize the policy via a parameter as , which allows us to determine the best action in a state.

The policy gradient method has several advantages, and it can handle the continuous action space where we have an infinite number of actions and states. Say we are building a self-driving car. A car should be driven without hitting any other vehicles. We get a negative reward when the car hits a vehicle and a positive reward when the car does not hit any...

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