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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 1326–1350 of 1718 papers

TitleStatusHype
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
Attention-Augmented Inverse Reinforcement Learning with Graph Convolutions for Multi-Agent Task Allocation—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks—0
Attention Loss Adjusted Prioritized Experience Replay—0
Attention Schema in Neural Agents—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
Best Possible Q-Learning—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning—0
Biases for Emergent Communication 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