N-Agent Ad Hoc Teamwork
arxiv(2024)
摘要
Current approaches to learning cooperative behaviors in multi-agent settings
assume relatively restrictive settings. In standard fully cooperative
multi-agent reinforcement learning, the learning algorithm controls
all agents in the scenario, while in ad hoc teamwork, the learning
algorithm usually assumes control over only a single agent in the
scenario. However, many cooperative settings in the real world are much less
restrictive. For example, in an autonomous driving scenario, a company might
train its cars with the same learning algorithm, yet once on the road, these
cars must cooperate with cars from another company. Towards generalizing the
class of scenarios that cooperative learning methods can address, we introduce
N-agent ad hoc teamwork, in which a set of autonomous agents must interact
and cooperate with dynamically varying numbers and types of teammates at
evaluation time. This paper formalizes the problem, and proposes the
Policy Optimization with Agent Modelling (POAM) algorithm. POAM is a
policy gradient, multi-agent reinforcement learning approach to the NAHT
problem, that enables adaptation to diverse teammate behaviors by learning
representations of teammate behaviors. Empirical evaluation on StarCraft II
tasks shows that POAM improves cooperative task returns compared to baseline
approaches, and enables out-of-distribution generalization to unseen teammates.
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