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 1201–1210 of 1718 papers

TitleStatusHype
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning—0
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?—0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming—0
O(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games—0
Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—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