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 16011625 of 1718 papers

TitleStatusHype
Universal Policies to Learn Them AllCode0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?Code0
Cooperative Artificial IntelligenceCode0
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile RobotsCode0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement LearningCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement LearningCode0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Conversational AI for Positive-sum Retailing under Falsehood ControlCode0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy OptimizationCode0
Learning to Gather without CommunicationCode0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Convergent Policy Optimization for Safe Reinforcement LearningCode0
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamicsCode0
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement LearningCode0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
Prosocial learning agents solve generalized Stag Hunts better than selfish onesCode0
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Benchmark Results

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