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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 801–825 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
Policy Diversity for Cooperative Agents—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—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
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-MakingCode0
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games—0
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of HanabiCode0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning—0
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy OptimizationCode0
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

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