reinforcement learning

DHRL-JARSS: SDN-based Deep Hierarchical Reinforcement Learning for QoS-aware Joint Access-Routing Scheduling in multi-link intelligent ship network

Autonomous global-minimum search of Pd6Mo7 nanoclusters from deep reinforcement learning

Autonomous sailing with sim-to-real reinforcement learning

Reinforcement learning based control of packed bed reactors for chemical looping combustion

Deep reinforcement learning for multi-Objective latency minimization in 5G heterogeneous vehicular networks

Multiple targets tracking with unmanned aerial vehicle swarm based on multi-agent deep reinforcement learning framework with safety network

Advancing AI‑powered hybrid plants: Reinforcement learning control of small‑scale solar Brayton turbines via surrogate modelling and fuel‑air ratio insensitivity discovery

Supplementary Data for: Mechanism-informed graph reinforcement learning for resource-constrained maintenance scheduling in IFMIF-DONES

This dataset contains the supplementary material supporting the manuscript "Mechanism-informed graph reinforcement learning for resource-constrained maintenance scheduling in IFMIF-DONES". It includes the Master Sequence of Operations (best-observed SOP) generated by the GCN-TD3 optimizer to achieve the 71.43-hour make...

自适应图神经网络的结构优化

This paper presents a novel approach to self-adaptive graph neural networks, aiming to automatically optimize the network structure based on data distribution. Traditional graph neural networks often require manual design of the network architecture, limiting their adaptability. Our proposed method employs an adaptive...

Scalable Modular Architectures for Naturalistic Behavior and Cognitive Mapping in Biological and Artificial Intelligence

Introduction Biological intelligence is far more adaptive, autonomous and efficient than current artificial intelligence, despite recent advances. Neuroethological comparisons and computational simulations show that nervous systems across phyla share a conserved modular organization for information flow from sensation...

Nonlinear Constraints and Adaptive Variable Weight Optimization

This paper explores a novel algorithm for non-linear constraint satisfaction, focusing on adaptive variable weight optimization. Traditional approaches often struggle with variables that change over time, leading to suboptimal solutions. We introduce a dynamic variable weighting mechanism that automatically adjusts wei...

Reviewer #1 (Public review): Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning