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 17111718 of 1718 papers

TitleStatusHype
On Centralized Critics in Multi-Agent Reinforcement LearningCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing PlatformsCode0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
Shaping Advice in Deep Reinforcement LearningCode0
A Generalist Hanabi AgentCode0
Uncoupled Learning of Differential Stackelberg Equilibria with CommitmentsCode0
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Benchmark Results

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