A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
Coordinated exploration via intrinsic rewards for multi-agent rein- forcement learning
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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.
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Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
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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.