Robust multi-agent reinforcement learning
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United States of America
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Apr 1, 2025
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app pub date -
Jun 22, 2020
filing date -
Jun 22, 2020
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Abstract
Techniques for robust multi-agent reinforcement learning (MARL) are described. An exemplary method includes initializing a plurality of parameters for a plurality of agents including at least policy parameters and action-value (Q) parameters; performing robust multi-agent reinforcement learning to learn polices for the agents, wherein in the learned polices no agent has an incentive to deviate, the agents include an implicit agent that is to select a worst-case at any given time during the learning process; and at least one agent utilizing its learned policy.
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