reinforcement learning

Pediatric Dentist–Parent Interactions: Converting the Thinking of Practitioners Into Learning Outcomes for Predoctoral Students

BACKGROUND: Effective interaction with parents is indispensable for dentists, yet little literature exists on skillsets or application. PURPOSES: (1) Identify core practices of pediatric dentists when interacting with parents; (2) summarize core practices for use in a skillset for students using a Novice to Expert mode...

Safe multi-agent motion planning in presence of FDIAs via Kalman filter and filtered reinforcement learning

This paper studies safe motion planning for multi-agent systems in complex environments under Gaussian noise and false data injection attacks (FDIAs). Such attacks corrupt sensor measurements, degrade state estimation, and impair collision avoidance. To address this problem, we propose a unified framework for secure es...

Code for: Discovering the Logic of the Environment through Explainable Neuro-Symbolic Reinforcement Learning

This repository contains the Python code, custom Gymnasium environments, and training scripts for the neuro-symbolic reinforcement learning framework described in the manuscript submitted.

ACC-projecting claustral neurons are required during cue pre-exposure for latent inhibition

In a noisy world, learning to ignore stimuli that repeatedly occur without consequence is essential for adaptive behavior. Latent inhibition, in which prior inconsequential cue exposure slows subsequent associative learning, captures this capacity. The claustrum is densely interconnected with frontal cortex and implica...

HALSI: Hierarchical adaptive latent skill inference for temporal abstraction in reinforcement learning

Dynamic federated multi-agent planning under partial observability and network instability for real-time edge applications

In recent years, the proliferation of autonomous systems at the edge—including robotic swarms, autonomous vehicles, and intelligent sensor networks—has highlighted the urgent need for resilient multi-agent planning under uncertain, decentralized conditions. Traditional multi-agent planning architectures typically assum...

Can Artificial Intelligence be Socratic?

It is argued that SI construct gives the philosophy of AI a normative vocabulary that interfaces classical philosophy with current machine-learning practice.

MIRAI-EDU-D: A Software-Free Discrete-Electronics Educational Robot for Tangible STEAM Formative Assessment

This article presents MIRAI-EDU-D, a software-free adaptive educational micro-robot designed to support tangible formative assessment in STEAM activities for primary and secondary education. The proposal addresses two current tensions: the expansion of educational robotics as a resource for active learning and computat...

Cache-Fused Kinematic Rails: Ex-Ante Silicon Alignment via In-Vivo KV-Cache Metric Grafting

Abstract of Cache-Fused Kinematic Rails: Ex-Ante Silicon Alignment via In-Vivo KV-Cache Metric Grafting Current Large Language Model (LLM) alignment relies primarily on post-hoc, output-level correction mechanisms such as external API meta-governors, Reinforcement Learning from Human Feedback (RLHF), or post-generation...

Energy-aware routing in underwater wireless sensor networks via temporal graph neural network and reinforcement learning

Underwater wireless sensor networks (UWSN) are characterized by dynamic network topology, limited node energy, and constrained communication capabilities, which make reliable and energy-efficient data transmission a significant challenge in time-varying underwater environments. Conventional routing approaches, usually...

DGend/justgrpo-smaller-essential: v1.1.0

Code accompanying the manuscript "Standard Group Relative Reinforcement Learning Is Unstable on Small Masked-Diffusion Language Models: A Controlled Study and Five Rejected Explanations," submitted to Scientific Reports. Includes the reinforcement learning driver, the supervised fine-tuning and evaluation scripts writt...

A model–free reinforcement learning framework for statistical process monitoring with variable sampling rate