reinforcement learning

A Deep Reinforcement Learning (DRL) Based Transformer Method for Solving the Open Shop Scheduling Problem

In this study, we investigate the potential of a Deep Reinforcement Learning (DRL) based Transformer neural network architecture to solve large-scale open shop scheduling problems. The open shop scheduling problem (OSSP) involves sequencing n jobs across m machines, where each machine handles only one job at a time, an...

Calibration Does Not Compose, Types Destroy Vagueness: The Hidden-Markov and Fuzzy Primitives Missing from System-One Decision Models

System-one decision models — fast, non-generative networks that emit typed, calibrated probabilistic decisions for machine-to-machine pipelines, of which TypeSafe AI's Jev, trained by Reinforcement Learning for Calibrated Decisions (RLCD), is the announced instance — are audited by hop-level calibration on stationary h...

A Comparative Analysis of Power-Delay-Area Improvements Between Traditional Heuristic and AI-Driven Electronic Design Automation Techniques

This paper presents a conceptual literature review and qualitative comparative synthesis of two major families of Electronic Design Automation (EDA) logic-optimization techniques: modern classical heuristic logic synthesis, grounded in scalable multi-level DAG-based optimization frameworks (Mishchenko et al., 2018; Ama...

When Agreement Becomes Governance: Sycophancy, Silence, and Upward Conformity as Information-Integrity Failures in Modern Bureaucracies—A Critical Review

Sycophancy is often treated as an undesirable interpersonal behavior involving flattery, excessive deference, or opportunistic agreement with powerful actors. This article argues that such a view is too narrow. Drawing on a critical qualitative literature review of organizational behavior, human resource management, le...

Optimizing and evaluating proximal policy optimization and soft actor-critic agents in Unity ML-Agents-based games

This study presents a comparative analysis of proximal policy optimization (PPO) and soft actor-critic (SAC) for training autonomous delivery agents in high-fidelity 3D environments using Unity ML-Agents. Both algorithms were evaluated with identical hyperparameters and reward functions across five independent runs to...

Rethinking content delivery for learning reinforcement: a reflective account of podcast summaries and Universal Design for Learning in Pharmacy Education

Purpose Maintaining student engagement with lecture content beyond the classroom remains a persistent challenge in higher education. While lecture recordings are widely used, they often require substantial time to revisit and may not align with students' study habits. This reflective article examines the introduction o...

A Comparative Analysis of Power-Delay-Area Improvements Between Traditional Heuristic and AI-Driven Electronic Design Automation Techniques

This paper presents a conceptual literature review and qualitative comparative synthesis of two major families of Electronic Design Automation (EDA) logic-optimization techniques: modern classical heuristic logic synthesis, grounded in scalable multi-level DAG-based optimization frameworks (Mishchenko et al., 2018; Ama...

Offline-to-online reinforcement learning with triple-intensity policy constraints

DRL-driven dynamic resource allocation for multimodal semantic communication and URLLC coexistence networks

The coexistence of multimodal semantic communication and ultra-reliable low-latency communication (URLLC) creates a resource management challenge for next-generation wireless networks. Semantic communication reduces transmission redundancy by delivering task-relevant information rather than complete bit streams, wherea...

Model-free optimal synchronization learning for heterogeneous multi-robots

This paper investigates the optimal synchronization control problem for heterogeneous multi-robot systems with unknown dynamics under a model-free reinforcement learning framework. Heterogeneous multi-robot systems have attracted increasing attention due to their broad applications in intelligent manufacturing, coopera...

Polymer informatics: A review of ai-driven structure-property modeling and material design

Polymer science is undergoing a profound paradigm shift from labor-intensive, empirical trial-and-error experimentation to AI-guided, data-driven, and predictive material design. Owing to their multiscale structural diversity (spanning atomic connectivity, chain-packing behavior, and macroscopic morphology), polymer ma...

AI Topics Module 2: Education

This textbook module examines the intersection of cognitive neuroscience, pedagogy, and artificial intelligence as they converge in the secondary school classroom. Beginning with the theory of mental rigor — the disciplined, sustained exertion of focused cognitive effort as a biological process with measurable neural c...