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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 1718 papers

TitleStatusHype
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization—0
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement LearningCode1
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure—0
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal HypergraphsCode0
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves—0
N-Agent Ad Hoc TeamworkCode1
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Differentially Private Reinforcement Learning with Self-Play—0
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management—0
Distributed Autonomous Swarm Formation for Dynamic Network Bridging—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Safety-Aware Multi-Agent Learning for Dynamic Network Bridging—0
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Paths to Equilibrium in Games—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Multi-agent transformer-accelerated RL for satisfaction of STL specifications—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
Learning and communication pressures in neural networks: Lessons from emergent communication—0
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence—0
Show:102550
← PrevPage 9 of 35Next →
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