Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning
CoRR(2024)
Abstract
Multi-agent deep reinforcement learning (MADRL) problems often encounter the
challenge of sparse rewards. This challenge becomes even more pronounced when
coordination among agents is necessary. As performance depends not only on one
agent's behavior but rather on the joint behavior of multiple agents, finding
an adequate solution becomes significantly harder. In this context, a group of
agents can benefit from actively exploring different joint strategies in order
to determine the most efficient one. In this paper, we propose an approach for
rewarding strategies where agents collectively exhibit novel behaviors. We
present JIM (Joint Intrinsic Motivation), a multi-agent intrinsic motivation
method that follows the centralized learning with decentralized execution
paradigm. JIM rewards joint trajectories based on a centralized measure of
novelty designed to function in continuous environments. We demonstrate the
strengths of this approach both in a synthetic environment designed to reveal
shortcomings of state-of-the-art MADRL methods, and in simulated robotic tasks.
Results show that joint exploration is crucial for solving tasks where the
optimal strategy requires a high level of coordination.
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