Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering
arXiv:2605.26286v1 Announce Type: cross Abstract: Real-world multi-agent reinforcement learning (MARL) systems must often operate under stale observations, stochastic communication delays, and intermittent packet loss. Policies trained under idealized synchronous conditions frequently exhibit significant performance degradation in these regimes because they act on outdated feedback. We propose a modular execution-stage state-estimation layer that replaces delayed communicated observations with current belief-state estimates. The framework integrates a learned Gated transition model with a recu
The increasing deployment of MARL systems in real-world scenarios necessitates solutions for robust operation under communication constraints like delays and packet loss.
This development allows for more reliable and performant multi-agent AI systems in non-ideal conditions, bridging the gap between theoretical training and practical application.
The ability to compensate for communication delays structurally improves the robustness and operational efficacy of MARL policies in complex, dynamic environments.
- · AI developers
- · Robotics companies
- · Logistics and autonomous systems sectors
- · Systems highly reliant on perfect real-time synchronization
- · Competitors without similar delay compensation mechanisms
Multi-agent systems will achieve higher performance and reliability in real-world deployments.
This enhanced reliability could accelerate the adoption of autonomous multi-agent systems in critical infrastructure and complex operational environments.
Increased robustness could lead to a societal reliance on increasingly complex and interconnected intelligent agent systems, demanding new safety and ethical frameworks.
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Read at arXiv cs.AI