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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 1718 papers

TitleStatusHype
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation—0
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Ontology-driven Reinforcement Learning for Personalized Student Support—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
A Review of the Applications of Deep Learning-Based Emergent Communication—0
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System—0
Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)—0
Coordination Failure in Cooperative Offline MARL—0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks—0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things—0
Tractable Equilibrium Computation in Markov Games through Risk Aversion—0
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
The Benefits of Power Regularization in Cooperative Reinforcement Learning—0
Show:102550
← PrevPage 24 of 69Next →
All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified