reinforcement learning

Optimizing Resource Allocation in Cloud Computing Environments using Reinforcement Learning

A deep reinforcement learning framework for intelligent cloud resource allocation that jointly optimizes resource utilization, Service Level Agreement compliance, infrastructure cost, and energy efficiency, and adapts to workload distribution shifts within 200 episodes without manual retuning is presented.

Comprehensive Review on Artificial Intelligence-Based Smart Healthcare Monitoring Using IoT and HCI

This review critically examines the integration of AI, IoT, and HCI in smart healthcare monitoring and identifies unresolved challenges involving dataset bias, sensor reliability, model drift, human over-reliance, alarm fatigue, interoperability, regulatory compliance, and equitable access.

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 16 academic APIs.

Steel plate allocation via heuristic-guided reinforcement learning

Order allocation in steel manufacturing constitutes a large-scale NP-hard combinatorial optimization problem. The task assigns heterogeneous subplates to motherplates to minimize material waste, directly influencing production cost. Existing approaches rely on simplified assumptions or predefined cutting patterns, whic...

AI-based smart load balancing for sustainable electric vehicle charging hubs using LSTM forecasting and reinforcement learning

An AI-Based Smart Load Balancing Electric Vehicle Charging Hub (AI-SLBECH), which offers an effective method for integrating all three service modalities (dispatch mode, predictive scheduling using LSTM models and optimal policies through PPO-RL algorithms), as well as real-time monitoring via Internet of Things (IoT)...

Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...

Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...

Intelligent Robotics in Manufacturing

Industry 5.0 signifies the emotional intelligence and human creativity which carries the exactness of intelligent machines. There are two core visions like bioeconomy and human–robot collaboration to focusing the technological advancement, sources of efficiency, and sustainability development. Therefore, forward Automa...

Hybrid Fuzzy-AI Control

This chapter explores hybrid fuzzy–AI control architectures that combine interpretable rule-based control with learning-based adaptation. It begins by reviewing the motivation for hybrid approaches, particularly in situations where fuzzy controllers provide interpretable baseline behavior but may have limitations under...

Temporal task graph-based multi-agent reinforcement learning for efficient human-robot collaborative assembly planning

Field-validated climate-resilient HVAC control for mixed-use buildings in a hot-arid region

Buildings in desert cities are difficult to operate efficiently because heat waves and airborne dust increase cooling demand, alter short-term thermal behavior, and reduce HVAC efficiency. This study develops and field-validates an adaptive control framework for mixed residential and commercial buildings in a hot-arid...

Distance-Aware Inverse Reinforcement Learning Dataset for Robotic Grasping

This dataset contains state-action observations collected from a low-cost robotic manipulator during expert-guided demonstrations for robotic grasping. Each observation records four joint measurements, the scanning-servo angle of the time-of-flight (ToF) distance sensor, the measured distance between the robot and the...