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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

Showing 251275 of 1718 papers

TitleStatusHype
Collaborative Visual NavigationCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy RegularizationCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Collaborating with Humans without Human DataCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Mean Field Multi-Agent Reinforcement LearningCode1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
Multi-Agent Constrained Policy OptimisationCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
On the Robustness of Cooperative Multi-Agent Reinforcement LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified