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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

Showing 15261550 of 1718 papers

TitleStatusHype
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss0
Multi-Robot Formation Control Using Reinforcement Learning0
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
“Other-Play” for Zero-Shot Coordination0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning0
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning0
Individual specialization in multi-task environments with multiagent reinforcement learners0
Fairness in Multi-agent Reinforcement Learning for Stock Trading0
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator0
Biases for Emergent Communication in Multi-agent Reinforcement Learning0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents0
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
Multi-Agent Deep Reinforcement Learning with Adaptive Policies0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense0
Multi-Agent Game Abstraction via Graph Attention Neural Network0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning0
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified