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arxiv: 1906.03063 · v1 · pith:4HH4SYWM · submitted 2019-06-06 · cs.LG · stat.ML

Classical Policy Gradient: Preserving Bellman's Principle of Optimality

Philip S. Thomas , Scott M. Jordan , Yash Chandak , Chris Nota , James Kostas This is my paper

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classification cs.LG stat.ML
keywords bellmangradientobjectiveoptimalityprinciplebettercapturesclassical
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We propose a new objective function for finite-horizon episodic Markov decision processes that better captures Bellman's principle of optimality, and provide an expression for the gradient of the objective.

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