Unverified50% confidenceFactExact time
Multi-agent reinforcement learning (MARL) faces optimization difficulties in cooperative tasks caused by the non-additivity of rewards.
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11/4/2026
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UnverifiedThe study adopts Multi-Agent Reinforcement Learning (MARL) as the training framework.76% similarUnverifiedCurriculum learning is necessary for sparse-reward cooperative tasks; directly fading out the auxiliary rule-based policy is less robust than staged replacement.73% similarUnverifiedCoM offers a lightweight idea: without complex reward shaping, learning efficiency can be improved simply by adjusting the difficulty order of training targets.72% similarVerifiedIn sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.70% similarUnverifiedThe MASCAL method lets each agent jointly optimize task performance and confidence calibration during reinforcement learning, learning to generate calibrated correct probabilities for its answers.69% similar
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