reinforcement learning

Sparse Additive Off-Policy Evaluation for Reinforcement Learning with Potentially Limited Number of Trajectories

A new framework for flexible, nonlinear, and interpretable off-policy evaluation for infinite-horizon reinforcement learning is developed and a group-sparsity-based feature screening procedure is proposed that identifies, with high probability, a reduced feature set containing all relevant covariates.

Progress and Analysis of Optimization for Large Language Models Based on Reinforcement Learning

The boundaries of how reinforcement learning optimizes the reasoning capabilities of large language models under different resource and scale constraints are reviewed, as well as the adaptability of reinforcement learning's optimization of large language models in different scenarios are analyzed.

Artificial Intelligence and Graph Theory Based Traffic Signal Control Systems for Road Intersections

Artificial Intelligence and Graph Theory Based Traffic Signal Control Systems for Road Intersections Traffic congestion is a critical and economically damaging problem in dense urban areas, driven by rapid urbanization, rising vehicle density, and — most critically — fixed-time signal strategies that fail to adapt to r...

Artificial Intelligence and Graph Theory Based Traffic Signal Control Systems for Road Intersections

Artificial Intelligence and Graph Theory Based Traffic Signal Control Systems for Road Intersections Traffic congestion is a critical and economically damaging problem in dense urban areas, driven by rapid urbanization, rising vehicle density, and — most critically — fixed-time signal strategies that fail to adapt to r...

Adaptive Anchor-Guided Spherical-Neutrosophic Intelligence for Uncertainty-Aware Engineering Systems — Research Software and Reproducibility Package

This repository provides the research software, reproducibility materials, benchmark results, and figure-generation resources accompanying the study “Adaptive Anchor-Guided Spherical-Neutrosophic Intelligence for Uncertainty-Aware Engineering Systems.” The package implements and supports the proposed Adaptive Anchor-Gu...

Generating realistic safety–critical scenarios for vehicle–pedestrian interactions

Automated driving system (ADS) deployment requires rigorous validation across safety–critical vehicle–pedestrian interactions, yet real-world datasets rarely capture high-risk scenarios while simulation platforms lack realistic behavior. In response, this study proposes a three-stage framework that combines real-world...

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...

Supplementary material from "Code-driven planning in grid worlds with large language models"

We propose an iterative programmatic planning (IPP) framework that solves grid-based tasks by using large language models (LLMs) to synthesize interpretable, executable programs that act as agent policies mapping environment states to action sequences, rather than relying on traditional search or reinforcement learning...

Power enhancement of a tuned inerter-equipped point-absorber wave energy converter using deep reinforcement learning

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...

Adaptive Q-learning and cross-layer queue management for UAV-assisted vehicular routing

Reinforcement learning-guided multi-objective trajectory planning for obstacle avoidance in robotic manipulators

This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator, which simultaneously minimizes path length, energy consumption, and execution time while satisfying collision-avoidance, kinematic, and dynamic constraints.