Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
arxiv(2024)
摘要
With the advancements of artificial intelligence (AI), we're seeing more
scenarios that require AI to work closely with other agents, whose goals and
strategies might not be known beforehand. However, existing approaches for
training collaborative agents often require defined and known reward signals
and cannot address the problem of teaming with unknown agents that often have
latent objectives/rewards. In response to this challenge, we propose teaming
with unknown agents framework, which leverages kernel density Bayesian inverse
learning method for active goal deduction and utilizes pre-trained,
goal-conditioned policies to enable zero-shot policy adaptation. We prove that
unbiased reward estimates in our framework are sufficient for optimal teaming
with unknown agents. We further evaluate the framework of redesigned
multi-agent particle and StarCraft II micromanagement environments with diverse
unknown agents of different behaviors/rewards. Empirical results demonstrate
that our framework significantly advances the teaming performance of AI and
unknown agents in a wide range of collaborative scenarios.
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