reinforcement learning

Hierarchical Reinforcement Learning with Intrinsic Motivation and Meta-Learning

This paper proposes a novel hierarchical reinforcement learning (HRL) framework that leverages intrinsic motivation, meta-learning, and a hierarchical architecture to address the limitations of traditional HRL methods concerning exploration and generalization. The core idea is to integrate these three components to cre...

Adaptive Resource Allocation in Cloud Computing (ARAC)

This paper presents Adaptive Resource Allocation in Cloud Computing (ARAC), a novel approach to cloud management that leverages reinforcement learning for dynamic resource allocation. Traditional cloud platforms often rely on manual configuration and pre-defined rules, leading to suboptimal resource utilization and pot...

SACCL: A reinforcement learning-based approach for radiation source localization

Adaptive Meta-Learning via Simulated Environment Dynamics

Meta-learning, the learning to learn, has shown significant promise in tackling complex tasks. However, a prevalent limitation lies in the reliance on static reward functions and environment dynamics, often simplifying the learning process and potentially hindering generalization to real-world scenarios. This paper int...

Massed and Distributed Training in a Canine Match-to-Sample Task: A Single-Subject Exploratory Study

The scheduling of practice may influence learning performance, behavioral engagement, and response speed across species. This exploratory study compared massed and distributed training in a visual match-to-sample task involving a three-year-old male mixed-breed domestic dog. A single-subject within-subject design was e...

Quantum-Cognitive Reinforcement Learning via Penrose Objective Reduction

Classical reinforcement learning (RL) and decision theory rely on Kolmogorovian probability spaces and independent utility metrics. These models fail to capture non-commutative cognitive framing, question order effects, and collective voter gridlocks observed in human surveys and Web3 decentralized autonomous organizat...

Sip and Strum: Matcha Intake and a Practice-Reduction Reinforcement Contingency in Beginner Ukulele Skill Acquisition

This study examined differences in beginner ukulele skill acquisition under three combinations of matcha intake and a practice-reduction reinforcement contingency. A quasi-experimental three-group design was employed involving 15 students aged 18–25 years with no prior musical-instrument experience. Participants were a...

Quantum-Cognitive Reinforcement Learning via Penrose Objective Reduction

Classical reinforcement learning (RL) and decision theory rely on Kolmogorovian probability spaces and independent utility metrics. These models fail to capture non-commutative cognitive framing, question order effects, and collective voter gridlocks observed in human surveys and Web3 decentralized autonomous organizat...

Cognitive UAV-driven agro-surveillance framework for predicting crop stress–induced yield loss using spatio-temporal learning and adaptive irrigation control

Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or indiv...

Explainable reinforcement learning designs a LRP1-targeted BBB-penetrating peptide with spatial selectivity for glioblastoma therapy

Knowledge-guided hierarchical reinforcement learning for robust operational control of chiller plants: Field deployment in a hotel building

Transformer-HAPPO: A convergent multi-agent reinforcement learning framework for large-scale single-track railway timetabling