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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 1331–1340 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Data sharing gamesCode0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with TransformersCode1
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Coding for Distributed 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