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 15011525 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
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Benchmark Results

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