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 591–600 of 1718 papers

TitleStatusHype
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
Nucleolus Credit Assignment for Effective Coalitions in 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