PALO bounds for reinforcement learning in partially observable stochastic games

Neurocomputing, pp. 36-56, 2021.

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Abstract A partially observable stochastic game (POSG) is a general model for multiagent decision making under uncertainty. Perkins’ Monte Carlo exploring starts for partially observable Markov decision process (POMDP) (MCES-P) integrates Monte Carlo exploring starts (MCES) into a local search of the policy space to offer an elegant tem...More



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