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Abstract

A method for Markov Decision Process (“MDP”) decomposition includes receiving data elements for a problem that include finite state data for a set of state variables and a finite set of actions. A portion of the state data corresponding to state variables represents states. The method incudes creating two or more sub-MDPs. Each sub-MDP includes a portion of the set of state variables, the set of actions and a same reward function. The method includes executing each sub-MDP. Results include a policy and an expected reward from the reward function. The policy of the sub-MDP maps states of the sub-MDP to actions. The method includes aggregating, based on the expected rewards of the results, the actions of the policies of the sub-MDPs to create a resultant policy with resultant actions and generating, using state entries for the set of state variables, results to the problem based on the resultant policy.

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