reinforcement learning

Physically assisted goal-conditioned multi-agent reinforcement learning for collaborative AUV path planning in the Internet of Underwater Things

Representation-Aware Test-Time Reinforcement Learning for LLMs

A deep reinforcement learning algorithm with heterogeneous graph and hierarchical attention mechanism for dynamic flexible job shop scheduling problem

2026 Interim Teaching Statement Update

Zenodo Description This Interim 2026 Teaching Statement documents the next stage in the evolution of my teaching and learning philosophy, moving from INTEGRATE to SUSTAIN. It is a self-directed biennial update to a philosophy first developed in 2018 through the Harvard University Derek Bok Center for Teaching and Learn...

Deep reinforcement learning for simulated autonomous heat stress mitigation in dairy cattle

Heat stress significantly constrains dairy cattle productivity and welfare, compromising milk production and fertility. Yet, despite advances in heat stress detection and prediction, conventional mitigation strategies still rely on fixed thresholds, overlooking inter-animal variability and cumulative thermal load. In t...

Constitutional Intelligence as Formal Science The Karimov–Alekberli Framework as Mathematical Foundation for Governed Institutional Cognition

We present a synthesis of the Karimov–Alekberli (KA) Framework into a unified theory ofConstitutional Intelligence: the capacity of an adaptive system to make consequentialdecisions that are simultaneously governed, explainable, accountable, reconstructable,verifiable, and capable of learning without retroactive falsif...

Intelligent Cloud Resource Allocation Using Reinforcement Learn-ing: Evaluating Accuracy, Adaptability, and Efficiency

Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...

Source Code for Adaptive Intelligent Resource Optimization in Wireless Sensor Networks Using Reinforcement Learning and Compressive Sensing

This repository contains the source code and computational implementation associated with the manuscript “Adaptive Intelligent Resource Optimization in Wireless Sensor Networks Using Reinforcement Learning and Compressive Sensing” submitted to Scientific Reports. The repository includes the proposed algorithm, preproce...

Deep reinforcement learning for simulated autonomous heat stress mitigation in dairy cattle

Heat stress significantly constrains dairy cattle productivity and welfare, compromising milk production and fertility. Yet, despite advances in heat stress detection and prediction, conventional mitigation strategies still rely on fixed thresholds, overlooking inter-animal variability and cumulative thermal load. In t...

Construction-to-lifecycle risk intelligence: a noise-robust Physics-Informed Temporal Graph Transformer and Bayesian Markov-Switching sample-selection framework for reinforcement and repair decisions

Construction projects require sequential decisions on structural reinforcement during execution and repair prioritization during operation under heterogeneous conditions, incomplete inspections, temporal dependence and non-engineering constraints. This study develops a construction-to-lifecycle risk-intelligence framew...

Explainable AI (XAI) Framework for Transparent Human–Machine Job Scheduling in Hybrid Printing Operations

Hybrid printing operations integrate conventional manufacturing, additive manufacturing, and digital printing technologies, to create complex scheduling challenges arising from dynamic production environments, heterogeneous resources, and the limited transparency of conventional Artificial Intelligence (AI)-based sched...

Functional alignment of protein language models via reinforcement learning

Protein language models (pLMs) enable generative design of novel protein sequences but remain fundamentally misaligned with protein engineering goals, as they lack explicit understanding of function and often fail to improve properties beyond those found in nature. We introduce Reinforcement Learning from eXperimental...