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 926–950 of 1718 papers

TitleStatusHype
Probe-Based Interventions for Modifying Agent Behavior—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
Online Learning in Unknown Markov Games—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?—0
Q-MARL: A GRAPH-BASED SOLUTION FOR LARGE-SCALE MULTI-AGENT REINFORCEMENT LEARNING INSPIRED BY QUANTUM CHEMISTRY—0
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning—0
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified