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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 451475 of 1718 papers

TitleStatusHype
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy OptimizationCode0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement LearningCode0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Heterogeneous Multi-agent Zero-Shot Coordination by CoevolutionCode0
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
Gifting in multi-agent reinforcement learningCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic GenerationCode0
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning SystemsCode0
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Concurrent Meta Reinforcement LearningCode0
Fully Independent Communication in Multi-Agent Reinforcement LearningCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent IntelligenceCode0
Finding Friend and Foe in Multi-Agent GamesCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
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Benchmark Results

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