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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 801–810 of 1718 papers

TitleStatusHype
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Policy Diversity for Cooperative Agents—0
MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange—0
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning—0
Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement LearningCode0
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control—0
Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach—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