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 1126–1150 of 1718 papers

TitleStatusHype
The challenge of hidden gifts in multi-agent reinforcement learning—0
The challenge of redundancy on multi-agent value factorisation—0
The Complexity of Markov Equilibrium in Stochastic Games—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Interpretable Learned Emergent Communication for Human-Agent Teams—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
The Power of Communication in a Distributed Multi-Agent System—0
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis—0
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning—0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control—0
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework—0
Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—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