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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

Deep Q-networks

If we recall Chapter 2Training Reinforcement Learning Agents Using OpenAI Gym, where we tried to implement a basic Q-network, we studied that for a real-world problem, Q-learning using a Q-table is not a feasible solution owing to continuous state and action spaces. Moreover, a Q-table is environment-specific and not generalized. Therefore, we need a model which can map the state information provided as input to Q-values of the possible set of actions. This is where a neural network comes to play the role of a function approximator, which can take state information input in the form of a vector, and learn to map them to Q-values for all possible actions.

Let's discuss the issues with Q-learning in a gaming environment and ...

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Publisher Resources

ISBN: 9781788835725Supplemental Content