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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 1701–1718 of 1718 papers

TitleStatusHype
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
Homeostatic Coupling for Prosocial Behavior—0
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Human-Machine Dialogue as a Stochastic Game—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Hybrid Training for Enhanced Multi-task Generalization in 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