SOTAVerified

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 801–850 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
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach—0
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
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
Intelligent Communication Planning for Constrained Environmental IoT Sensing with 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
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
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems—0
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes—0
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning—0
VISER: A Tractable Solution Concept for Games with Information AsymmetryCode0
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Enhancing the Robustness of QMIX against State-adversarial Attacks—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Discovering Causality for Efficient Cooperation in Multi-Agent EnvironmentsCode0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—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