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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
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
Deep Reinforcement Learning, a textbook—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified