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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 1426–1450 of 1718 papers

TitleStatusHype
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning—0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading—0
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications—0
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Coordination-driven learning in multi-agent problem spaces—0
Coordination Failure in Cooperative Offline MARL—0
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients—0
CORD: Generalizable Cooperation via Role Diversity—0
Correcting Experience Replay for Multi-Agent Communication—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Credit Assignment with Meta-Policy Gradient for 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