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 401–425 of 1718 papers

TitleStatusHype
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
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
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
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal HypergraphsCode0
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
Show:102550
← PrevPage 17 of 69Next →
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