Decision-making with Speculative Opponent Models

arXiv:2211.11940v2 Announce Type: replace-cross
Abstract: Opponent modeling has benefited a controlled agent's decision-making by constructing models of other agents. Existing methods commonly assume access to opponents' observations and actions, which is infeasible when opponents' behaviors are unobservable or hard to obtain. We propose a novel multi-agent distributional actor-critic algorithm to achieve speculative opponent modeling with purely local information (i.e., the controlled agent's observations, actions, and rewards). Specifically, the actor maintains a speculated belief of the opponents, which we call the …

abstract actor actor-critic agent agents algorithm arxiv cs.ai cs.lg cs.ma decision making modeling multi-agent novel type