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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

In this chapter, we covered the details of a gridworld type of environment and understood the basics of the Markov decision process, that is, states, actions, rewards, transition model, and policy. Moreover, we utilized this information to calculate the utility and optimal policy through value iteration and policy iteration approaches.

Apart from this, we got a basic understanding of what partially observable Markov decision processes look like and the challenges in solving them. Finally, we took our favorite gridworld environment from OpenAI gym, that is, FrozenLake-v0 and implemented a value iteration approach to make our agent learn to navigate that environment.

In the next chapter, we will start with policy gradients and move ...

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Publisher Resources

ISBN: 9781788835725Supplemental Content