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The design of autonomous agents that can interact effectively with otheragents without prior coordination is a core problem in multi-agent systems.Type-based reasoning methods achieve this by maintaining a belief over a set ofpotential behaviours for the other agents. However, current methods are limitedin that they assume full observability of the state and actions of the otheragent or do not scale efficiently to larger problems with longer planninghorizons. Addressing these limitations, we propose Partially ObservableType-based Meta Monte-Carlo Planning (POTMMCP) - an online Monte-Carlo TreeSearch based planning method for type-based reasoning in large partiallyobservable environments. POTMMCP incorporates a novel meta-policy for guidingsearch and evaluating beliefs, allowing it to search more effectively to longerhorizons using less planning time. We show that our method converges to theoptimal solution in the limit and empirically demonstrate that it effectivelyadapts online to diverse sets of other agents across a range of environments.Comparisons with the state-of-the art method on problems with up to $10^{14}$states and $10^8$ observations indicate that POTMMCP is able to compute bettersolutions significantly faster.

Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments