SOTAVerified

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 12261250 of 1718 papers

TitleStatusHype
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer0
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Communicating via Markov Decision ProcessesCode0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning0
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study0
Collaborative Visual NavigationCode1
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments0
Federated Dynamic Spectrum Access0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows0
Many Agent Reinforcement Learning Under Partial Observability0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning0
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A New Formalism, Method and Open Issues for Zero-Shot CoordinationCode0
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Metric Policy Representations for Opponent Modeling0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective0
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Benchmark Results

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