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reinforcement learning

2,493 papers

#reinforcement learning Open access Oct 2026

RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning

Modern REST API testing faces a critical challenge in defining reliable test oracles, particularly in agile industrial environments where formal specifications (e.g., OpenAPI) are frequently missing or outdated, and historical execution logs are unavailable for newly deployed endpoints. In this paper, we present RESTOR...

Xun Zhou, Zhen Dong, Mingyu Ren et al. · 0 citations
#reinforcement learning Open access Oct 2026

Applied behaviour analysis of owner-dog interactions: assessment and intervention for behavior change, and animal welfare

Dogs were the first species to be domesticated, and while other species perish due to anthropogenic influences, dogs thrive in the human-dominated environment. Notwithstanding, companion dogs can develop problem behaviours, such as aggressive responses, destructiveness, or stereotypic behaviours. Canine behaviour probl...

Nicole Pfaller-Sadovsky · 0 citations
#reinforcement learning Open access Oct 2026

Reconfigurable intelligent surface and UAV-assisted communications: a deep reinforcement learning approach

This thesis proposes novel methods based on the deep reinforcement learning algorithms (DRL) for maximising the energy efficiency (EE), sum-rate in reconfigurable intelligent surface (RIS) and unmanned aerieal vehicles (UAV)-aided wireless communications. The thesis carries out comprehensive optimization and evaluation...

Khoi Khac Nguyen · 0 citations
#reinforcement learning Open access Oct 2026

Machine learning supported multiple criteria decision making for project delivery system selection

The construction industry is a backbone of the national economy throughout the world as it accounts for a sizeable proportion of most countries' gross domestic product. It is generally accepted that construction is a project-based industry, and the selection of project delivery system (PDS) is one of the most important...

Xingyu Zhu · 0 citations
#reinforcement learning Open access Oct 2026

Towards safety-critical control of autonomous systems

The rapid development of autonomous systems has revealed significant technological potential across numerous domains, yet safety concerns continue to pose a substantial barrier to their widespread adoption. Autonomous systems are defined here as engineered systems capable of performing tasks and making decisions indepe...

Stephen McIlvanna · 0 citations
#reinforcement learning Open access Oct 2026

Unitary fault-tolerant encoding of Pauli states in surface codes

In fault-tolerant quantum computation, the preparation of logical states is a ubiquitous subroutine, yet significant challenges persist even for the simplest states required. In the present work, we present a unitary, scalable, distance-preserving encoding scheme for preparing Pauli eigenstates in surface codes. Unlike...

Luis Colmenárez, Remmy A. M. Zen, Jan Ollé et al. · 0 citations
#reinforcement learning Open access Oct 2026

Can a blended learning environment enhance teaching and learning in large heterogeneous first-year classes?: a study in a private higher education institution

This research was conceptualised to enhance the educational experience of first-year undergraduate students. The massification of higher education has meant that first-year students are often commonly taught with students from different faculties in large lecture theatres. As the drive towards an outcome-based student-...

Angela Lincoln · 0 citations
#reinforcement learning Open access Oct 2026

Using Reinforcement Learning in Solving Exam Timetabling Problems

There is increasing interest in the area of research involving automated technologies for solving problems. High generality and ease of reusability have become a major goal in the development of Search methodologies within automation. One search method that meets these requirements is the hyper-heuristic. A hyper-heuri...

Kehan Han · 0 citations
#reinforcement learning Open access Oct 2026

ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs

Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees about a model’s ability to withstand different adversarial perturbations, has rarely been addressed in quantization due to the unacceptably...

Yuchen Yang, Yifan Zhao, Shubham Ugare et al. · 1 citation
#reinforcement learning Open access Oct 2026

Penguatan Kompetensi Digital Pendidik Nonformal melalui Pelatihan Pengembangan Sumber Belajar Digital Berbasis Learning by Doing

Limited skills among nonformal educators in developing digital technology-based learning resources remain a challenge in improving the quality of learning in equivalency education. This community engagement program aimed to improve nonformal educators’ knowledge and skills in designing, developing, and integrating digi...

Arif Sugianto, Wahdatan Nisa, Mustangin Mustangin et al. · 0 citations

SCRAMBLE: Safe Cooperative Risk-Aware Multi-Agent Belief Learning for Embodied Reinforcement Learning in Partially Observable Dynamic Environments

Safe decision-making in partially observable dynamic environments remains a fundamental challenge for multi-agent embodied reinforcement learning, where agents must cooperate with limited local observations while responding to evolving environmental changes and safety-critical interactions. Existing methods often empha...

Bo-Zhi Zhang, Jiang-Bo Wang, Tian Jing · 0 citations
#generative ai Book Open access Oct 2026

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

artificial intelligence (AI) and machine learning (ML) have become general-purpose technologies that are reshaping how organisations make decisions, serve customers, design products and run operations. This chapter provides a management-oriented treatment of the principal AI and ML techniques and of the organisational...

Shashank Saroop, Sunil Singarapu · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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