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 651–675 of 1718 papers

TitleStatusHype
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Mediated Multi-Agent Reinforcement LearningCode0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning—0
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory ManagementCode1
Provably Learning Nash Policies in Constrained Markov Potential Games—0
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications—0
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement LearningCode1
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents—0
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization—0
Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle PursuitCode1
Inductive Bias for Emergent Communication in a Continuous Setting—0
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement LearningCode1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
Improving the generalizability and robustness of large-scale traffic signal control—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning—0
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode0
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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