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AI Research
arXiv CS.AI·May 27·5 min read
Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering
TL;DR
Multi-agent reinforcement learning (MARL) policies trained in ideal conditions fail when deployed with communication delays and packet loss because they act on stale observations. A modular state-estimation layer was built to replace delayed observations with current belief-state estimates at execution time.
✦ Why It Matters
Engineers can deploy pre-trained MARL policies in real networks with delays without expensive retraining by adding a learned state-estimation layer.
Key Takeaways
- Insert a learned dynamics filter at execution time to estimate current state from delayed observations.
- Pre-trained MARL policies can be enhanced without retraining by adding a modular state-estimation layer.
- Test policies under realistic communication delays and packet loss during deployment validation.
- Use belief-state estimates instead of stale communicated observations to maintain multi-agent coordination.
Full Summary
Multi-agent reinforcement learning systems must coordinate multiple learning agents, but real deployments suffer from communication delays (stale observations arrive late) and packet loss that ideal training environments don't simulate. When policies trained under synchronous, perfect-information conditions encounter these delays, performance degrades significantly because agents make decisions on outdated state information.
The authors propose a decoupled delay-compensation framework: a learned dynamics filtering layer inserted at execution time that estimates the current true state from delayed observations, replacing stale inputs before the policy acts. This approach is modular—it works with existing pre-trained policies without requiring retraining.
The method filters observations through learned dynamics models to predict what the current state should be, compensating for communication lag. Results demonstrate that policies enhanced with this execution-stage layer recover performance degradation caused by realistic network conditions, enabling deployment of MARL systems in environments with latency and packet loss.