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 1176–1200 of 1718 papers

TitleStatusHype
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Learning Homophilic Incentives in Sequential Social Dilemmas—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Sequential Communication in Multi-Agent Reinforcement Learning—0
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Surprise Minimizing Multi-Agent Learning with Energy-based Models—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management—0
Offline Pre-trained Multi-Agent Decision Transformer—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
Evaluating Robustness of Cooperative MARL—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement LearningCode2
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates—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