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