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Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 626–650 of 1718 papers

TitleStatusHype
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning with State UncertaintyCode1
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems—0
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes—0
VISER: A Tractable Solution Concept for Games with Information AsymmetryCode0
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path FindingCode1
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Enhancing the Robustness of QMIX against State-adversarial Attacks—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARLCode1
Discovering Causality for Efficient Cooperation in Multi-Agent EnvironmentsCode0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified