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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

The Q-learning approach to reinforcement learning

Q-learning is an attempt to learn the value Q(s,a) of a specific action given to the agent in a particular state. Consider a table where the number of rows represent the number of states, and the number of columns represent the number of actions. This is called a Q-table. Thus, we have to learn the value to find which action is the best for the agent in a given state.

Steps involved in Q-learning:

  1. Initialize the table of Q(s,a) with uniform values (say, all zeros).

  2. Observe the current state, s

  3. Choose an action, a, by epsilon greedy or any other action selection policies, and take the action

  4. As a result, a reward, r, is received and a new state, s', is perceived

  5. Update the Q value ...

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Publisher Resources

ISBN: 9781788835725Supplemental Content