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

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Showing 1601–1650 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
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement LearningCode0
Smart Traffic Signals: Comparing MARL and Fixed-Time StrategiesCode0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement LearningCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile CommunicationsCode0
A Regularized Opponent Model with Maximum Entropy ObjectiveCode0
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode0
Proximal Learning With Opponent-Learning AwarenessCode0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Toward Policy Explanations for Multi-Agent Reinforcement LearningCode0
Transformer World Model for Sample Efficient Multi-Agent Reinforcement LearningCode0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement LearningCode0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning AlgorithmsCode0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based ControlCode0
Learning Sparse Graphon Mean Field GamesCode0
Multi-Agent Trust Region Policy OptimizationCode0
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified