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 971–980 of 1718 papers

TitleStatusHype
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles—0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Policy Evaluation in Decentralized POMDPs with Belief SharingCode0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Uncoupled Learning of Differential Stackelberg Equilibria with CommitmentsCode0
Offline Learning in Markov Games with General Function Approximation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified