reinforcement learning

Explainable reinforcement learning for smart production scheduling in automotive manufacturing

Supplementary document for Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC - 8037132.pdf

8 pages and 7 sections

Balancing solution quality and runtime in modular mobile hospital location: a hybrid approach of deep reinforcement learning and integer programming

A graph-based reinforcement-learning framework for logical qubit allocation in fault-tolerant quantum architectures

A graph-based reinforcement-learning framework is proposed in which a graph neural network is first pretrained to predict the ancilla-qubit cost of circuit-allocation pairs from allocation-aware circuit graphs, and the resulting encoder is transferred to a reinforcement learning agent that assigns tiles one at a time.

Toward trustworthy adaptive software reliability engineering: An explainable deep reinforcement learning framework

Ensuring the dependable operation of modern software systems under dynamic and non-stationary operating conditions remains a major challenge in software reliability engineering. Although recent deep reinforcement learning (DRL)-based approaches have demonstrated promising capabilities for closed-loop adaptation of soft...

Multi-Agent Reinforcement Learning for Stochastic OSAT Dispatching: A Matched Architecture Benchmark

Physics-prior-driven distributed deep reinforcement learning for multi-UAV path planning

A physics-prior-driven decentralized deep reinforcement learning (DRL) framework Functioning as a scalable distributed computing paradigm via decentralized training with decentralized execution (DTDE), the framework mitigates the curse of dimensionality.

Review of key technologies for robot embodied intelligence oriented toward flexible manufacturing

The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning.

CoSIPR: Shared Service Orchestration with Dynamic Interest Coalitions in Edge Computing

Experiments show that CoSIPR improves service-category matching, request satisfaction, and the average number of accepted requests per instance, and reduces aggregate state-transfer cost and limits hotspot exposure while maintaining a controlled trade-off between end-to-end delay and state-transfer cost.

Instilling Character Education Values through the Traditional Game of Selodor for Fourth-Grade Students at MI Al-Ijtihad Jontak

Traditional games remain underutilized in educational settings despite their potential to foster character development through experiential learning and social interaction. This study examines the implementation of the traditional selodor game in character education, identifies the character values manifested during pl...

Supplementary document for Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC - 8037132.pdf

8 pages and 7 sections

Behavior-based hybrid-action deep reinforcement learning for intelligent building energy management