The Focusing Influence Mechanism (FIM) uses an entropy-based criterion and eligibility traces to help multiple agents in reinforcement learning focus and maintain their influence on under-explored parts of the state space, improving coordinated exploration and performance under sparse rewards.
Understanding individual agent importance in multi-agent system via counterfactual reasoning
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Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
The Focusing Influence Mechanism (FIM) uses an entropy-based criterion and eligibility traces to help multiple agents in reinforcement learning focus and maintain their influence on under-explored parts of the state space, improving coordinated exploration and performance under sparse rewards.