reinforcement learning

Multi-agent meta-reinforcement learning based on contrastive predictive coding—For rapid adaptive traffic control after emergencies

A multi-agent meta-reinforcement learning framework is developed using Contrastive Predictive Coding for traffic state representation that maintained emergency vehicle priority while keeping the effect on regular traffic within a comparatively limited range under the evaluated emergency conditions.

Politeness toward artificial intelligence and the risks of anthropomorphic bias

Against the prevalent social norm of treating conversational artificial intelligence with politeness, this paper explores the cognitive risks brought by human habitual politeness toward chatbots such as ChatGPT. Drawing on historical analogies, virtue ethics and the mechanism of Reinforcement Learning from Human Feedba...

Curiosity-Guided Graph Neural Skill Discovery for Sparse-Reward Goal-Oriented Dialogue Systems

Goal-oriented chatbots often operate in sparse-reward settings, where meaningful feedback (task success or user satisfaction) arrives only after many dialogue turns. This makes exploration and policy learning challenging. We propose a novel hierarchical reinforcement learning (HRL) framework that performs automated ski...

Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC

We report an experimental implementation of deep reinforcement learning (RL) for optimizing the loading stage of a ⁶Li magneto-optical trap (MOT) in a high-dimensional continuous control space. An off-policy actor-critic agent observes in-situ fluorescence images at 111 ms intervals and updates seven experimental param...

Implementing the Reinforcement Method to Motivate Fifth-Grade Students in Arabic Language Learning at SDIT Al-Munir Pekanbaru Riau

This study aims to find out the application of the reinforcement method in giving motivation to learning Arabic among fifth grade students at Al-Munir Islamic Primary School in Pekanbaru, as well as to reveal the supporting and hindering factors in its application. The study was based on a qualitative approach with a d...

Solving the hybrid action space problem in edge computing: a graph-aware reinforcement learning approach for container management

The Synergistic Policy network for Edge Resource Control (SPERC) is presented, a novel deep reinforcement learning framework designed to solve this joint problem of ensuring performance through appropriate computational resource allocation and minimizing storage costs through image layer sharing.

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

8 pages and 7 sections

PEMBENTUKAN KEMANDIRIAN ANAK USIA 4-5 TAHUN DI TPA DAN TK IT AS-SALAM KOTA MALANG

This study was motivated by the importance of developing independence among children aged 4–5 years through habituation and environmental support. It aimed to describe the formation of child independence in the daycare and kindergarten environments of TK IT As-Salam Malang City and the factors influencing it. This rese...

Navigating the smart tourism era: a systematic review of how intelligent systems are shaping the future of travel

Tourism recommendation systems use artificial intelligence (AI), machine learning (ML), and data analytics to support travel decisions. This systematic literature review examines decision types, disciplinary distribution, themes, and research gaps in AI-based tourism recommendation research. Following the Preferred Rep...

EFFICIENT ENERGY MANAGEMENT IN NETWORKED MICROGRIDS USING MULTI-AGENT DEEP REINFORCEMENT LEARNING IN THE PRESENCE OF UNCERTAINTIES

Reinforcement Learning-Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics

Abstract The computation of electronic excited states and real-time quantum dynamics of many-Fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state prob...

Joint Optimization of Delay and Energy Efficiency for UAV Task Offloading and Cooperative Scheduling

The growing demand for multimedia services in Internet of Things (IoT) networks has significantly increased the traffic load on backhaul links, making Mobile Edge Caching (MEC) a key technology for reducing content delivery latency. Unmanned Aerial Vehicles (UAVs) can serve as mobile aerial caching nodes that complemen...