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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 931–940 of 1718 papers

TitleStatusHype
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RLCode1
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model—0
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum GamesCode0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NCode1
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
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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