Decentralized Multi-Agents by Imitation of a Centralized Controller

TL;DR

This paper introduces a flexible framework for multi-agent reinforcement learning that combines centralized training with decentralized execution, using imitation learning to enable agents to act independently.

Contribution

It proposes a novel algorithm that leverages centralized training to guide decentralized agents via imitation learning, applicable with any RL and imitation algorithms.

Findings

The framework achieves decentralized multi-agent solutions through imitation learning.

The method provides theoretical bounds for the effectiveness of the approach.

It demonstrates flexibility by not requiring specific structures in the algorithms used.

Abstract

We consider a multi-agent reinforcement learning problem where each agent seeks to maximize a shared reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent is situated in a non-stationary and partially-observable environment. In order to obtain multi-agents that act in a decentralized manner, we introduce a novel algorithm under the popular framework of centralized training, but decentralized execution. This training framework first obtains solutions to a multi-agent problem with a single centralized joint-space learner, which is then used to guide imitation learning for independent decentralized multi-agents. This framework has the flexibility to use any reinforcement learning algorithm to obtain the expert as well as any imitation learning algorithm to obtain the decentralized agents. This is in contrast to other multi-agent learning algorithms that, for example, can require more specific structures. We present some theoretical bounds for our method, and we show that one can obtain decentralized solutions to a multi-agent problem through imitation learning.