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

• URL: http://arxiv.org/abs/2505.03586v3
• Date: Mon, 12 May 2025 09:11:20 GMT
• Title: Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation
• Authors: Songchen Fu, Siang Chen, Shaojing Zhao, Letian Bai, Ta Li, Yonghong Yan,
• Abstract summary: In real-world multi-agent systems, observation delays are ubiquitous, preventing agents from making decisions based on the environment's true state.<n>These discrete observation components with varying delay characteristics pose significant challenges for multi-agent reinforcement learning.<n>We first formulate the decentralized individual delay partially observable decision process (DSID-POMDP) by extending the standard Dec-POMDP.<n>We then propose the Rainbow Delay Compensation (RDC) framework for addressing individual delays, along with recommended implementations for its constituent modules.
• Score: 10.511062258286335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 generalizability. Our work provides a novel perspective on multi-agent delayed observation problems and offers an effective solution framework. The source code is available at https://anonymous.4open.science/r/RDC-pymarl-4512/.

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