reinforcement learning

Implementasi Komunitas Belajar dalam Sekolah di SMK Swasta DKI Jakarta: Analisis Tiga Lapis Faktor dengan Kerangka Van Meter dan Van Horn

This study examines the factors causing implementation problems of the school-based learning community policy in private vocational high schools (SMK) in DKI Jakarta and formulates strategies to address them. The 2023 survey from the Center for Educational Standards and Policy identifies SMK as the level with the great...

Concept of a Fire Control System for Anti-Aircraft Artillery with Elements of Artificial Intelligence

This paper presents the concept and architecture of a Fire Control System (FCS) and the ballistic computation algorithms for anti-aircraft artillery. Traditional fire control systems rely on analytical methods that combine Kalman filter-based target tracking with physics-based projectile trajectory models to determine...

Diffusion models in robotics: a comprehensive review

Diffusion models are a powerful class of generative models that emerge in recent years to transform Gaussian noise into samples of the target distribution through an iterative denoising process. Due to the high training stability and powerful generative capabilities, diffusion models have surpassed previous generative...

TINYRLAM: A Resource-Efficient Reinforcement Learning Agent for Adaptive Energy Management in Energy-Harvesting Wireless Sensor Networks

Autonomous Scientific Discovery via Exploration Schema Libraries: Replicating Scientists' Problem-Space Metacognition

Current Artificial Intelligence for Science (AI4S) frameworks excel at pattern recognition within massive datasets but fundamentally lack causal reasoning and the capacity for autonomous theoretical innovation. To address this limitation, we introduce a novel paradigm that shifts the focus from learning scientific data...

LEARNING-BASED OPTIMIZATION OF DIGITAL HARDWARE: AI METHODS FOR SYNTHESIS, PHYSICAL DESIGN, AND VERIFICATION

Abstract The increasing complexity of digital systems has made hardware optimization a learning problem as much as an algorithmic one. Conventional electronic design automation (EDA) flows depend on manually engineered heuristics that may not generalize across architectures, workloads, and technology nodes. This journa...

Machine Learning in Personalized Drug-Dose Optimization: A Review

Dosing decisions for narrow-therapeutic-index and highly variable drugs have historically relied on population-based nomograms, therapeutic drug monitoring, and clinician heuristics, which frequently under- or over-dose individual patients. Machine learning (ML) offers a data-driven alternative capable of integrating g...

A Digital Twin Framework for Autonomous Kinetic Facades: Bio-inspired SMA Actuation with Reinforcement Learning Control for Tropical Climates

Building envelope automation is limited by heavy motor-driven shading and reactive rule-based controllers. This paper presents an autonomous Digital Twin framework for kinetic facades, integrating bio-inspired Shape-Memory Alloy [SMA] actuation with a Reinforcement Learning [RL] control policy. Conceived as a Robotic S...

AI-Based Optimization of ALU Power Consumption in Modern Computer Architectures

To address the power efficiency bottlenecks and thermal design limits inherent in modern sub-micron microprocessors, this paper introduces an AI-driven optimization framework designed to minimize dynamic and static power dissipation in multi-bit Arithmetic Logic Units (ALUs) without sacrificing operating frequency or t...

Project TALOS: Tactical Agentic Literature Orchestration System

Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.

Oleer: optimized layer-based and energy-efficient routing protocol with enabled Q-learning for underwater sensor networks

A graph-aware Type-2 fuzzy reinforcement clustering framework for adaptive and hardware-efficient industrial internet of things networks