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Reinforcement Learning with TensorFlow
book

Reinforcement Learning with TensorFlow

by Sayon Dutta
April 2018
Intermediate to advanced content levelIntermediate to advanced
334 pages
10h 18m
English
Packt Publishing
Content preview from Reinforcement Learning with TensorFlow

Summary

We knew that reinforcement learning optimizes the reward for an agent in the environment, and the Markov decision process (MDP) is a type of environment representation and mathematical framework for modeling the decisions using states, actions, and rewards. In this chapter, we understood that Q-learning is an approach that finds the optimal action selection policy for any MDP without any transition models. On the other hand, value iteration finds the optimal action selection policy for any MDP if a transition model is given.

We also learned another important topic called the deep-Q network, which is a modified Q-learning approach that takes a deep neural network as a function approximator to generalize across different environments, ...

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Publisher Resources

ISBN: 9781788835725Supplemental Content