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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 1281–1290 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems—0
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Multi-Agent Reinforcement Learning for Joint Police Patrol and Dispatch—0
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application—0
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching—0
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs—0
Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills—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