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 1401–1450 of 1718 papers

TitleStatusHype
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Data sharing gamesCode0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Coding for Distributed Multi-Agent Reinforcement Learning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Difference Rewards Policy Gradients—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Online Learning in Unknown Markov Games—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—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