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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 851–875 of 1718 papers

TitleStatusHype
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System—0
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Center of Gravity-Guided Focusing Influence Mechanism for Multi-Agent Reinforcement Learning—0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control—0
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems—0
Decentralized Multi-Agents by Imitation of a Centralized Controller—0
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering—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