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A policy improvement method in constrained stochastic dynamic programming
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DOI：10.1109/TAC.2006.880801.png)
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This note presents a formal method of improving a given base-policy such that the performance of the resulting policy is no worse than that of the base-policy at all states in constrained stochastic dynamic programming. We consider finite horizon and discounted infinite horizon cases. The improvement method induces a policy iteration-type algorithm that converges to a local optimal policy.
Keyword:
constrained Markov decision process
dynamic programming
policy improvement
policy iteration
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7
论文数:
1.3W
被引数:
6.7W
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