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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 601–625 of 1718 papers

TitleStatusHype
Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement LearningCode0
MolOpt: Autonomous Molecular Geometry Optimization using 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
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
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
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning—0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
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
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation—0
MARLIM: Multi-Agent Reinforcement Learning for Inventory Management—0
Robust Multi-Agent Reinforcement Learning with State UncertaintyCode1
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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