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 671–680 of 1718 papers

TitleStatusHype
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
Improving the generalizability and robustness of large-scale traffic signal control—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning—0
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode0
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities—0
Attention Schema in Neural Agents—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