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 751–800 of 1718 papers

TitleStatusHype
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape EstimationCode1
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023Code0
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human SpacesCode1
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
Evolutionary Reinforcement Learning: A Survey—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning—0
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
Revisiting the Gumbel-Softmax in MADDPGCode1
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Differentiable Arbitrating in Zero-sum Markov Games—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Learning a Multi-Agent Controller for Shared Energy Storage System—0
Scalable Multi-Agent Reinforcement Learning with General Utilities—0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach—0
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility—0
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement LearningCode0
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation—0
Universal Agent Mixtures and the Geometry of Intelligence—0
Low Entropy Communication in Multi-Agent Reinforcement Learning—0
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems—0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Policy Evaluation in Decentralized POMDPs with Belief SharingCode0
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles—0
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective—0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
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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