ARMS is an automatic reward-shaping framework for sparse-reward MARL that uses trajectory ranking and conditional best-response reasoning to preserve Nash equilibria while improving sampling efficiency in pathfinding tasks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.
citing papers explorer
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ARMS: Automatic Reward Shaping for Sparse-Reward Multi-Agent Reinforcement Learning
ARMS is an automatic reward-shaping framework for sparse-reward MARL that uses trajectory ranking and conditional best-response reasoning to preserve Nash equilibria while improving sampling efficiency in pathfinding tasks.
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Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.