April 2018
Intermediate to advanced
334 pages
10h 18m
English
Content preview from Reinforcement Learning with TensorFlow
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Asynchronous one-step SARSA
The architecture of asynchronous one-step SARSA is almost similar to the architecture of asynchronous one-step Q-learning, except the way target state-action value of the current state is calculated by the target network. Instead of using the maximum Q-value of the next state s' by the target network, SARSA uses 
-greedy to choose the action a' for the next state s' and the Q-value of the next state action pair, that is, Q(s',a';
) is used to calculate the target state-action value of the current state.
The pseudo-code ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.