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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 976–1000 of 1718 papers

TitleStatusHype
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with TransformerCode1
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Policy Optimization for Markov Games: Unified Framework and Faster Convergence—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Multi-Agent Reinforcement Learning is a Sequence Modeling ProblemCode2
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
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Off-Beat Multi-Agent Reinforcement Learning—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
QGNN: Value Function Factorisation with Graph Neural NetworksCode1
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Learning to Advise and Learning from Advice in Cooperative 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