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Hands-On Reinforcement Learning with Python
book

Hands-On Reinforcement Learning with Python

by Sudharsan Ravichandiran
June 2018
Intermediate to advanced content levelIntermediate to advanced
318 pages
9h 24m
English
Packt Publishing
Content preview from Hands-On Reinforcement Learning with Python

Value iteration

In value iteration, we start off with a random value function. Obviously, the random value function might not be an optimal one, so we look for a new improved value function in iterative fashion until we find the optimal value function. Once we find the optimal value function, we can easily derive an optimal policy from it:

The steps involved in the value iteration are as follows:

  1. First, we initialize the random value function, that is, the random value for each state.
  2. Then we compute the Q function for all state action pairs of Q(s, a).
  3. Then we update our value function with the max value from Q(s,a).
  4. We repeat these steps ...
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Publisher Resources

ISBN: 9781788836524Supplemental Content