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Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
MARS replaces ratio-based trust regions with a multiplicatively symmetric geometric barrier to cut variance and prevent probability collapse in multi-agent policy optimization. Across 47 tasks it matches or exceeds MAPPO and MASPO, with gains from barrier geometry rather than flexible boundaries.
Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 3/5