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This work introduces sIPOMDPLite-net, a deep neural network (DNN)architecture for decentralized, self-interested agent control in partiallyobservable stochastic games (POSGs) with sparse interactions between agents.The network learns to plan in contexts modeled by the interactive partiallyobservable Markov decision process (I-POMDP) Lite framework and useshierarchical value iteration networks to simulate the solution of nested MDPs,which I-POMDP Lite attributes to the other agent to model its behavior andpredict its intention. We train sIPOMDPLite-net with expert demonstrations onsmall two-agent Tiger-grid tasks, for which it accurately learns the underlyingI-POMDP Lite model and near-optimal policy, and the policy continues to performwell on larger grids and real-world maps. As such, sIPOMDPLite-net shows goodtransfer capabilities and offers a lighter learning and planning approach forindividual, self-interested agents in multiagent settings.

SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions