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 1161–1170 of 1718 papers

TitleStatusHype
The Complexity of Markov Equilibrium in Stochastic Games—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—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