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 1401–1425 of 1718 papers

TitleStatusHype
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking—0
COMPOSER: Scalable and Robust Modular Policies for Snake Robots—0
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
Learning Multi-Agent Coordination through Connectivity-driven Communication—0
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks—0
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Cooperative Actor-Critic via TD Error Aggregation—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—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