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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 351375 of 1718 papers

TitleStatusHype
Light Aircraft Game : Basic Implementation and training results analysisCode0
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement LearningCode0
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Learning with Opponent-Learning AwarenessCode0
Data sharing gamesCode0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Learning Transferable Cooperative Behavior in Multi-Agent TeamsCode0
Learning to Gather without CommunicationCode0
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate ConvergenceCode0
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic RewardsCode0
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
Learning Sparse Graphon Mean Field GamesCode0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
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Benchmark Results

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