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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

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Showing 1476–1500 of 1718 papers

TitleStatusHype
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning—0
Gifting in multi-agent reinforcement learningCode0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
Learning Expensive Coordination: An Event-Based Deep RL Approach—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
Multi-agent Reinforcement Learning for Networked System Control—0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Who2com: Collaborative Perception via Learnable Handshake CommunicationCode1
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified