ARMS: Automatic Reward Shaping for Sparse-Reward Multi-Agent Reinforcement Learning

ArXi:2605.23562v1 Announce Type: cross Sparse rewards are a major bottleneck in multi-agent reinforcement learning (MARL), where simultaneous learning induces non-stationarity and makes reward design especially delicate. Reward shaping can accelerate learning, but in the multi-agent setting it must preserve the strategic structure of the problem rather than merely improve short-term optimization.