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A local approximation dynamic programming approach to policy evaluation

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LocalApproximationPolicyEvaluation.jl

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A local approximation dynamic programming approach to policy evaluation. This algorithm computes the expected return at each state given a probability distribution over actions.

This code is adapted from: https://github.com/JuliaPOMDP/LocalApproximationValueIteration.jl

Usage Notes

  • Requires a generative MDP with a discrete action space
  • Requires a function that computes the log-probability of each action
  • Returns a policy that
    1. Can have an action sampled according to the expected return of each action
    2. Can have the value of the state or state-action computed
  • See test/gridworld_test.jl for a usage example on a gridworld problem.

Maintained by Anthony Corso (acorso@stanford.edu)

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