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 1001–1025 of 1718 papers

TitleStatusHype
Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior—0
Risk-Aware Distributed Multi-Agent Reinforcement Learning—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—0
Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents—0
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning—0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—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