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Title: Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length. The challenge increases greatly in the multi-agent reinforcement learning (MARL) setting where the transition probabilities, observation kernel, and reward function are unknown. Here, we develop a general compression framework with approximate common and private state representations, based on which decentralized policies can be constructed. We derive the optimality gap of executing dynamic programming (DP) with the approximate states in terms of the approximation error parameters and the remaining time steps. When the compression is exact (no error), the resulting DP is equivalent to the one in existing work. Our general framework generalizes a number of methods proposed in the literature. The results shed light on designing practically useful deep-MARL network structures under the "centralized learning distributed execution" scheme.  more » « less
Award ID(s):
1955777 2038416 2008130
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
25th International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2022, Valencia, Spain. PMLR
Medium: X
Sponsoring Org:
National Science Foundation
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