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

Showing 251260 of 1718 papers

TitleStatusHype
Collaborating with Humans without Human DataCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Collaborative Visual NavigationCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic RewardCode1
Show:102550
← PrevPage 26 of 172Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified