Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

NIPS 2020, 2020.

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We argue that coordinating the agents' policies can guide their exploration and we investigate techniques to promote such an inductive bias

Abstract:

In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents. While the tractability of independent agent-wise exploration is appealing, this approach fails on tasks that require elaborate group strategies....More

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