reinforcement learning

Intelligent Feeding Pattern Optimization in Aquaculture Using Deep Reinforcement Learning

Equity-Aware Planning of Urban Ultra-Fast Electric Vehicle Charging Infrastructure with Spatial Graph Learning and Grid Constraints

Urban ultra-fast-charging planning must connect equitable user service with physically deliverable charging power. This study formulates a heterogeneous graph linking demand zones, roads, candidate stations, and distribution buses, together with an infrastructure model for station locations, port-level power tiers, int...

Reinforcement learning-assisted walrus optimization algorithm for multi-equipment optimization in industrial cooling water systems

In industrial refrigeration plant cooling water systems, chillers, cooling water pumps, and cooling towers are strongly coupled, making independent equipment regulation insufficient for global optimality. Existing chiller plant control studies often insufficiently characterize cross time thermal coupling caused by cool...

The influence of a research-informed professional development course on Iranian in-service EFL teachers’ cognitions and reported practices regarding oral corrective feedback

Abstract This study examines the influence of a research-informed professional development (PD) course on Iranian in-service EFL teachers’ cognitions and reported practices regarding oral corrective feedback (OCF). Data were collected through pre- and post-course interviews, reflective diaries, and online discussions w...

A Case Study on the Robustness of Traffic Signal Control from a Data Science Perspective: The RobustLight Diffusion Reinforcement Learning Framework

Damaged sensor data, often caused by noise interference and data loss, impairs the decision-making accuracy of Deep Reinforcement Learning (DRL) traffic signal control models. This paper takes RobustLight (ICML 2025) as the core case and analyzes it from a data science perspective. We introduce DRL-based traffic signal...

AI Alignment Training Paradigms and Cross-Model Differences in AI Persuasive Behavior

Reinforcement learning from human feedback (RLHF) and constitutional artificial intelligence (CAI) are two ways to shape model behavior. Among them, CAI is built around avoiding different categories of harm, including racism, sexism, toxic, dangerous and illegal content. This article integrates the existing empirical r...

A new statistical feature-based adaptive deep reinforcement learning model for multi-objective task scheduling in IoT-fog environments

Dynamic control and optimization model of accounting information disclosure quality of listed companies based on deep reinforcement learning

The goal of this study is to develop an intelligent dynamic control and optimization model using Deep Reinforcement Learning (DRL) technology to address the issues of low efficiency and poor adaptability of static models in environmental Accounting Information Disclosure (AID) audits of listed companies. Based on the t...

Esports trainer

Real-Time Strategy (RTS) ist ein Genre von Videospielen, das nicht in Runden ablaeuft; dies impliziert, dass das Spiel standardmaessig in Echtzeit gespielt wird. RTS-Spiele erfordern kontinuierliche, sofortige Entscheidungsfindungen, was sich von Brettspielen wie Schach oder Go unterscheidet, bei denen Spieler Zeit hab...

Scored Before the Question Exists: What a Write-Time Importance Value in LLM Agent Memory Predicts, Why an Additive Term Is Not a Prior, and the Ablation the Canonical Architecture Did Not Run

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....

Prior knowledge-assisted reinforcement learning for vehicle platoon control in cut-in scenarios

Energy Management for Ship Integrated Power Systems via Mode-Aware Safe Reinforcement Learning

Energy management in ship integrated power systems (IPSs) requires real-time dispatch of diesel generators, battery storage, and shore power under strict operational constraints. Existing deep reinforcement learning (DRL) approaches are economically competitive but cannot guarantee that device constraints are satisfied...