SOTAVerified

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 776–800 of 1718 papers

TitleStatusHype
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning—0
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization—0
Sample-Efficient Multi-Agent RL: An Optimization Perspective—0
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem—0
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility—0
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control—0
COMPOSER: Scalable and Robust Modular Policies for Snake Robots—0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
Attention Loss Adjusted Prioritized Experience Replay—0
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks—0
Dynamic Handover: Throw and Catch with Bimanual Hands—0
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate ConvergenceCode0
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—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