Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning

2026-05-05 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new framework addresses exploration challenges in cooperative multi-agent reinforcement learning (MARL) by dynamically adjusting exploration intensity. The method combines a Return-Conditioned Beta (RCB) schedule, which uses a sigmoid function based on team return to adapt global exploration intensity, with a per-agent Reward Signal Quality (RSQ) metric. RSQ, derived from signal-to-noise ratio (SNR) statistics of intrinsic rewards, allocates exploration budget by concentrating it on agents with reliable intrinsic reward signals. The framework integrates Successor Distance (SD) as the intrinsic reward mechanism, which naturally produces distinguishable per-agent signal quality. Evaluated on seven cooperative benchmarks, including MPE, SMAX, and MABrax tasks, the method achieves top-tier returns, outperforming existing baselines by providing both global intensity control and per-agent budget allocation.

Key takeaway

For research scientists developing cooperative MARL systems, this framework offers a robust approach to exploration. You should consider implementing the Return-Conditioned Beta (RCB) schedule for global intensity control and the Reward Signal Quality (RSQ) metric for per-agent budget allocation. This combination, particularly with Successor Distance (SD) as the intrinsic reward, can significantly enhance coordination and stability, especially in tasks requiring tight spatial coordination or large-scale agent interaction, preventing training collapse from noisy exploration signals.

Key insights

Dynamically adjusting exploration intensity globally and per-agent based on reward signal quality improves MARL performance.

Principles

• Exploration intensity should adapt to learning progress.
• Allocate exploration budget based on intrinsic reward signal reliability.
• Intrinsic reward methods must yield distinguishable signal quality.

Method

The framework uses a return-conditioned sigmoid schedule (RCB) for global intensity, and a per-agent Reward Signal Quality (RSQ) metric, based on signal-to-noise ratio, to modulate exploration. Successor Distance (SD) provides the intrinsic reward.

In practice

• Use Return-Conditioned Beta (RCB) for global exploration scheduling.
• Implement Reward Signal Quality (RSQ) to attenuate noisy agents.
• Employ Successor Distance (SD) for intrinsic reward generation.

Topics

• Cooperative MARL
• Exploration Scheduling
• Intrinsic Motivation
• Reward Signal Quality
• Successor Distance

Code references

• DH-O/RRS

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.