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 1101–1125 of 1718 papers

TitleStatusHype
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems—0
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula—0
State-based Episodic Memory for Multi-Agent Reinforcement Learning—0
Statistical discrimination in learning agents—0
Steganography in Game Actions—0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling—0
Stock market microstructure inference via multi-agent reinforcement learning—0
Strategic bidding in freight transport using deep reinforcement learning—0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
Surprise Minimizing Multi-Agent Learning with Energy-based Models—0
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training—0
Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Symmetries-enhanced Multi-Agent Reinforcement Learning—0
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning—0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
TAR^2: Temporal-Agent Reward Redistribution for Optimal Policy Preservation in Multi-Agent Reinforcement Learning—0
TarMAC: Targeted Multi-Agent Communication—0
Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning—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