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 1351–1375 of 1718 papers

TitleStatusHype
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance—0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation—0
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System—0
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Center of Gravity-Guided Focusing Influence Mechanism for 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