LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
In cooperative multi-agent reinforcement learning (MARL), where agents only have access to partial observations, efficiently leveraging local information is critical. During long-time observations, agents can build \textit{awareness} for teammates to alleviate the problem of partial observability. However, previous MARL methods usually neglect this kind of utilization of local information. To address this problem, we propose a novel framework, multi-agent \textit{Local INformation Decomposition for Awareness of teammates} (LINDA), with which agents learn to decompose local information and build awareness for each teammate. We model the awareness as stochastic random variables and perform representation learning to ensure the informativeness of awareness representations by maximizing the mutual information between awareness and the actual trajectory of the corresponding agent. LINDA is agnostic to specific algorithms and can be flexibly integrated to different MARL methods. Sufficient experiments show that the proposed framework learns informative awareness from local partial observations for better collaboration and significantly improves the learning performance, especially on challenging tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
InformativenessMulti-agent Reinforcement LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR
Developing scalable and efficient reinforcement learning algorithms for cooperative multi-agent control has received significant attention over the past years. Existing literature has proposed inexact decompositions of l…
Computational EfficiencyWhat's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem
The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring…
Decision MakingFairnessLINDA: Unsupervised Learning to Interpolate in Natural Language Processing
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have th…
Data Augmentationtext-classificationText ClassificationLINDA: Unsupervised Learning to Interpolate in Natural Language Processing
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have th…
Data Augmentationtext-classificationText ClassificationQTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition
In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, …
Multi-agent Reinforcement LearningSMACSMAC+