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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 1126–1150 of 1718 papers

TitleStatusHype
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—0
Independent and Decentralized Learning in Markov Potential Games—0
Multi-agent Databases via Independent Learning—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Off-Beat Multi-Agent Reinforcement Learning—0
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Learning Progress Driven Multi-Agent CurriculumCode0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement LearningCode0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
General sum stochastic games with networked information flows—0
Conversational AI for Positive-sum Retailing under Falsehood ControlCode0
Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multi-Agent Reinforcement LearningCode0
Toward Policy Explanations for 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