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 351400 of 1718 papers

TitleStatusHype
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Mediated Multi-Agent Reinforcement LearningCode0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement LearningCode0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement LearningCode0
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
Data sharing gamesCode0
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate ConvergenceCode0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic RewardsCode0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
Light Aircraft Game : Basic Implementation and training results analysisCode0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Learning Transferable Cooperative Behavior in Multi-Agent TeamsCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
Learning with Opponent-Learning AwarenessCode0
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
Learning to Gather without CommunicationCode0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic GamesCode0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
Learning Sparse Graphon Mean Field GamesCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement LearningCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum GamesCode0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized TeamingCode0
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning SystemsCode0
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Benchmark Results

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