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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 201225 of 1718 papers

TitleStatusHype
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation ControlCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
Off-Policy Multi-Agent Decomposed Policy GradientsCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural NetworkCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Phantom -- A RL-driven multi-agent framework to model complex systemsCode1
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle PursuitCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
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Benchmark Results

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