Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2605.09212v1
• Date: Sat, 09 May 2026 23:14:27 GMT
• Title: Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
• Abstract summary: Ratio-based trust-region methods update decentralized actors using per-agent probability ratios weighted by joint advantage estimates.<n>We introduce Multi-Agent Ratio Symmetry (MARS), a novel policy optimization objective that replaces these additive ratio-based trust-region mechanisms with a multiplicatively symmetric geometric barrier.<n>MARS matches or exceeds MAPPO and MASPO in aggregate environment-level performance.
• Score: 8.199581456016062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Centralized training with decentralized execution (CTDE) is a standard framework for cooperative multi-agent policy-gradient reinforcement learning, allowing agents to learn from joint information while acting from local observations. Ratio-based trust-region methods such as Multi-Agent Proximal Policy Optimization (MAPPO) and Multi-Agent Simple Policy Optimization (MASPO) update decentralized actors using per-agent probability ratios weighted by joint advantage estimates. Teammate non-stationarity increases the variance of these advantages, which in turn increases the variance in the local ratio updates. This exposes two method-specific failure modes: MAPPO's additive clipping removes gradients for outlier samples and weakens recovery from policy drift, while MASPO's soft quadratic penalty can allow probability collapse. We introduce Multi-Agent Ratio Symmetry (MARS), a novel policy optimization objective that replaces these additive ratio-based trust-region mechanisms with a multiplicatively symmetric geometric barrier. MARS preserves corrective gradients while assigning unbounded cost as probability ratios approach zero. Across 47 tasks spanning eight multi-agent environments, including novel JAX benchmarks PaxMen and AeroJAX, MARS matches or exceeds MAPPO and MASPO in aggregate environment-level performance. Ablations show that these gains arise from the geometry of the symmetric barrier rather than from flexible trust-region boundaries alone.

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