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 15011550 of 1718 papers

TitleStatusHype
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal HypergraphsCode0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average ConstraintsCode0
Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement LearningCode0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Explainable Multi-Agent Reinforcement Learning for Temporal QueriesCode0
Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviorsCode0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Modelling crypto markets by multi-agent reinforcement learningCode0
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation StudyCode0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
Mediated Multi-Agent Reinforcement LearningCode0
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
Deep Multi-Agent Reinforcement Learning with Relevance GraphsCode0
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized TeamingCode0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Shapley Q-value: A Local Reward Approach to Solve Global Reward GamesCode0
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention MechanismsCode0
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Multi-Agent Advisor Q-LearningCode0
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous VehiclesCode0
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualityCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Multi-Agent Common Knowledge Reinforcement LearningCode0
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement LearningCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning SystemsCode0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
Deep Meta Coordination Graphs for Multi-agent Reinforcement LearningCode0
Deep Coordination GraphsCode0
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive ReasoningCode0
Evolution of Societies via Reinforcement LearningCode0
Investigating Relational State Abstraction in Collaborative MARLCode0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convectionCode0
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic GamesCode0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Adaptive and Robust DBSCAN with Multi-agent Reinforcement LearningCode0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov GamesCode0
Show:102550
← PrevPage 31 of 35Next →

Benchmark Results

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