arXiv:2605.23562v1 Announce Type: cross Abstract: 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. We propose Automatic Reward-shaping in Multi-agent Systems (ARMS), a self-supervised reward shaping framework for MARL that learns dense shaping signals from sparse environmental rewards th
The increasing complexity and practical applications of multi-agent reinforcement learning (MARL) in real-world systems necessitate more robust and efficient training methods, particularly for sparse-reward environments.
This development addresses a critical bottleneck in MARL, making it more feasible to deploy intelligent autonomous agents in complex scenarios by automating the previously difficult and time-consuming reward engineering process.
Reward shaping, historically a delicate and manual process in MARL, can now be self-supervised and automated, significantly accelerating the development and deployment of sophisticated multi-agent AI systems.
- · AI developers
- · Robotics companies
- · Logistics and supply chain sector
- · Autonomous systems integrators
- · Companies relying on manual reward engineering for MARL
- · Traditional AI optimization methods without automated shaping
More efficient and scalable training of complex multi-agent AI systems becomes possible.
Accelerated deployment of advanced AI agents in diverse applications, from manufacturing to autonomous vehicles, becoming more capable with less human oversight.
A potential increase in the sophistication and autonomy of AI agents could further drive the convergence towards general-purpose AI and impact labor markets.
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Read at arXiv cs.AI