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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 Introduction to Reinforcement Learning Getting Started with OpenAI and TensorFlow The Markov Decision Process and Dynamic Programming Gaming with Monte Carlo Methods Temporal Difference Learning Multi-Armed Bandit Problem Deep Learning Fundamentals Atari Games with Deep Q Network Playing Doom with a Deep Recurrent Q Network The Asynchronous Advantage Actor Critic Network Policy Gradients and Optimization Capstone Project – Car Racing Using DQN Recent Advancements and Next Steps Assessments Other Books You May Enjoy

TD control

In TD prediction, we estimated the value function. In TD control, we optimize the value function. For TD control, we use two kinds of control algorithm:

  • Off-policy learning algorithm: Q learning
  • On-policy learning algorithm: SARSA

Q learning

We will now look into the very popular off-policy TD control algorithm called Q learning. Q learning is a very simple and widely used TD algorithm. In control algorithms, we don't care about state value; here, in Q learning, our concern is the state-action value pair—the effect of performing an action A in the state S.

We will update the Q value based on the following equation:

The preceding equation is similar to the TD prediction update rule with a little difference...

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