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 576–600 of 1718 papers

TitleStatusHype
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control—0
COMPOSER: Scalable and Robust Modular Policies for Snake Robots—0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy RegularizationCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat ManeuveringCode1
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Attention Loss Adjusted Prioritized Experience Replay—0
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks—0
Dynamic Handover: Throw and Catch with Bimanual Hands—0
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—0
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate ConvergenceCode0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Policy Diversity for Cooperative Agents—0
MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange—0
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning—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