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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 591–600 of 1718 papers

TitleStatusHype
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
From Motor Control to Team Play in Simulated Humanoid Football—0
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey—0
Generalisable Agents for Neural Network Optimisation—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