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 1651–1700 of 1718 papers

TitleStatusHype
Multi-lingual agents through multi-headed neural networksCode0
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned IncentivesCode0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Multiple Landmark Detection using Multi-Agent Reinforcement LearningCode0
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement LearningCode0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
Gifting in multi-agent reinforcement learningCode0
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning SystemsCode0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value DecompositionCode0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic GenerationCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous RoboticsCode0
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation AssuranceCode0
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Scaling Laws for a Multi-Agent Reinforcement Learning ModelCode0
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning SystemsCode0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement LearningCode0
Towards Fault Tolerance in Multi-Agent Reinforcement LearningCode0
Concurrent Meta Reinforcement LearningCode0
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of HanabiCode0
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control SystemCode0
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement LearningCode0
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-MakingCode0
Fully Independent Communication in Multi-Agent Reinforcement LearningCode0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
Self-Motivated Multi-Agent ExplorationCode0
Self-Organized Polynomial-Time Coordination GraphsCode0
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized CriticsCode0
Learning Progress Driven Multi-Agent CurriculumCode0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement LearningCode0
Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multi-Agent Reinforcement LearningCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
Finding Friend and Foe in Multi-Agent GamesCode0
Collaborative Information Dissemination with Graph-based 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