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 1226–1250 of 1718 papers

TitleStatusHype
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Multi-Agent Automated Machine Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Multi-agent Continual Coordination via Progressive Task Contextualization—0
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search—0
Multi-agent Databases via Independent Learning—0
Multi-agent Deep Covering Skill Discovery—0
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning—0
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Multi-Agent Deep Reinforcement Learning with Adaptive Policies—0
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Multi-Agent Game Abstraction via Graph Attention Neural Network—0
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Multi-Agent Informational Learning Processes—0
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—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