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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

On-policy and off-policy learning

Off-policy learning as the name suggests, is the learning of optimal policy independent of the agent's actions. Therefore, you don't need a specific policy to start with and the agent will learn the optimal policy even by starting with a random action, finally converging to the optimal one. Q-learning is an example of off-policy learning.

On the other hand, on-policy learning learns the optimal policy by carrying out the current policy and updating it through exploration methods. Thus, on-policy learning is dependent on the policy you start with. The SARSA algorithm is an example of on-policy learning.

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Publisher Resources

ISBN: 9781788835725Supplemental Content