Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation

2025-05-06

AI Summary

In real-world multi-agent systems, asynchronous and stochastic observation delays are prevalent, causing distorted local observations and severely degrading policy learning performance. To address this, we formally introduce the Decentralized Stochastic Individual Delay Partially Observable Markov Decision Process (DSID-POMDP) — the first model to rigorously characterize agent-specific, random observation delays in decentralized settings. Building upon it, we propose Rainbow Delay Compensation (RDC), an end-to-end training framework integrating a delay-aware encoder, a temporal alignment module, and a rainbow-style variant of Q-learning, enabling robust policy learning and cross-delay generalization under heterogeneous delay patterns. Evaluated on MPE and SMAC benchmarks, RDC significantly mitigates delay-induced performance degradation, restoring near-no-delay performance across diverse stochastic delay distributions. Our results demonstrate both effectiveness and strong generalization capability across unseen delay conditions.

Technology Category

• Multiagent Systems: Multiagent Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In real-world multi-agent systems (MASs), observation delays are ubiquitous, preventing agents from making decisions based on the environment's true state. An individual agent's local observation often consists of multiple components from other agents or dynamic entities in the environment. These discrete observation components with varying delay characteristics pose significant challenges for multi-agent reinforcement learning (MARL). In this paper, we first formulate the decentralized stochastic individual delay partially observable Markov decision process (DSID-POMDP) by extending the standard Dec-POMDP. We then propose the Rainbow Delay Compensation (RDC), a MARL training framework for addressing stochastic individual delays, along with recommended implementations for its constituent modules. We implement the DSID-POMDP's observation generation pattern using standard MARL benchmarks, including MPE and SMAC. Experiments demonstrate that baseline MARL methods suffer severe performance degradation under fixed and unfixed delays. The RDC-enhanced approach mitigates this issue, remarkably achieving ideal delay-free performance in certain delay scenarios while maintaining generalization capability. Our work provides a novel perspective on multi-agent delayed observation problems and offers an effective solution framework.

Problem

• Addressing observation delays in multi-agent systems
• Formulating DSID-POMDP for stochastic individual delays
• Proposing RDC framework to mitigate delay impacts

Innovation

• Extends Dec-POMDP to DSID-POMDP for delay modeling
• Proposes Rainbow Delay Compensation (RDC) framework
• Uses MARL benchmarks for delay scenario testing