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 751–775 of 1718 papers

TitleStatusHype
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape EstimationCode1
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023Code0
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human SpacesCode1
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
Evolutionary Reinforcement Learning: A Survey—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning—0
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—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