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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 1081–1090 of 1718 papers

TitleStatusHype
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control—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