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 1571–1580 of 1718 papers

Title	Date	Tasks	Status
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning	Nov 11, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation	Nov 3, 2019	Multi-agent Reinforcement LearningReinforcement Learning	—Unverified
Deep Decentralized Reinforcement Learning for Cooperative Control	Oct 29, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Convergent Policy Optimization for Safe Reinforcement Learning	Oct 26, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding	Oct 25, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation	Oct 21, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning	Oct 19, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
MAVEN: Multi-Agent Variational Exploration	Oct 16, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available
RLCard: A Toolkit for Reinforcement Learning in Card Games	Oct 10, 2019	Board GamesCard Games	CodeCode Available
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching	Oct 7, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified