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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
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning—0
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning—0
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems—0
Regularization of the policy updates for stabilizing Mean Field Games—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
Discovering Causality for Efficient Cooperation in Multi-Agent EnvironmentsCode0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian LearningCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
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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