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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 426–450 of 1718 papers

TitleStatusHype
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management—0
Distributed Autonomous Swarm Formation for Dynamic Network Bridging—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Safety-Aware Multi-Agent Learning for Dynamic Network Bridging—0
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Paths to Equilibrium in Games—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Multi-agent transformer-accelerated RL for satisfaction of STL specifications—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
Learning and communication pressures in neural networks: Lessons from emergent communication—0
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence—0
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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