Multi-Agent Interactions Modeling with Correlated Policies
- URL: http://arxiv.org/abs/2001.03415v3
- Date: Thu, 11 Jun 2020 11:22:24 GMT
- Title: Multi-Agent Interactions Modeling with Correlated Policies
- Authors: Minghuan Liu, Ming Zhou, Weinan Zhang, Yuzheng Zhuang, Jun Wang,
Wulong Liu, Yong Yu
- Abstract summary: In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework.
We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL)
Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
- Score: 53.38338964628494
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In multi-agent systems, complex interacting behaviors arise due to the high
correlations among agents. However, previous work on modeling multi-agent
interactions from demonstrations is primarily constrained by assuming the
independence among policies and their reward structures. In this paper, we cast
the multi-agent interactions modeling problem into a multi-agent imitation
learning framework with explicit modeling of correlated policies by
approximating opponents' policies, which can recover agents' policies that can
regenerate similar interactions. Consequently, we develop a Decentralized
Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL),
which allows for decentralized training and execution. Various experiments
demonstrate that CoDAIL can better regenerate complex interactions close to the
demonstrators and outperforms state-of-the-art multi-agent imitation learning
methods. Our code is available at \url{https://github.com/apexrl/CoDAIL}.
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