reinforcement learning

Game-theoretic and multi-agent reinforcement learning-based adaptive optimization of multi-aggregator virtual power plants in coupled electricity, carbon, and green certificate markets

Automated Dense Reward Function Design for Reinforcement Learning in Autonomous Driving

Crafting reward functions remains a challenge in reinforcement learning, particularly in autonomous driving (AD), where safety, efficiency, and task completion must be balanced. We introduce a GPT-5.6-based revision of AD Reward Designer, a framework for generating interpretable dense reward functions from natural lang...

Groundhog Loops: A Time-Loop Testbed for Measuring Learning Speed and Tactic Formation in AI Agents

The project defines a test environment inspired by the time-loop mechanism featured in the film Groundhog Day. An agent attempts to complete a task; if it makes an error classified as fatal (or exhausts its step budget), the environment is restored to a previously saved checkpoint, while the agent retains a complete, s...

SAFE-AD: Socially aware field-enhanced reinforcement learning for autonomous driving in interactive traffic

Code for: The Value of Health Information in Couples' Annuity Choice: A Dyadic Mortality Model, an Exact Benchmark, and Explainable Reinforcement Learning

Code that produces every result, table and figure of the article: the cohort of couples built from the RAND HRS Longitudinal File, a Bayesian mortality model of couples and its assessment, the couple's annuity decision solved by dynamic programming with exact expectations, two reinforcement learning agents, and Shapley...

Paradigma Belajar dan Pembelajaran: Tinjauan Teoritis dan Psikologis

This study examines learning and instructional paradigms from a theoretical and psychological perspective. The aim of the study is to understand how classical and modern learning theories can be integrated with students' psychological aspects, resulting in a more comprehensive learning paradigm. The research method use...

Optimal control-guided reinforcement learning for turbojet engine speed regulation

Hierarchical reinforcement learning with model predictive elevator control for risk-sensitive multi-robot transport scheduling in hospitals

Hospitals increasingly deploy autonomous mobile robots (AMRs) to move specimens and medicines between wards and laboratories. These fleets must meet clinical deadlines that differ across request classes, which couples two decisions: which request each robot serves, and the order of its stops. Intra-hospital transport i...

Optimism Needs a Way Back: What Value an Agent Should Give a State It Did Not Know Existed, Why Every Located Guarantee Borrows Its Answer From a Reset, a Data Horizon or a Reference, and What That Leaves for Unknown Unknowns

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Life of Research A learning agent that reaches a state outside everything it has represented must give that state a value before it can act again, and the reinforcement learning literature offers three answers. Optimism values the unknown high so that the agent goe...

Bridging the grid-thermal-computational nexus via physics-informed hierarchical multi-agent reinforcement learning for data center co-optimization

Experiment-compatible measurement-feedback quantum state preparation with reinforcement learning

Abstract Ground-state preparation is a critical task in quantum simulation and quantum computing, as it enables the study of correlated phases and the generation of entangled resource states. While measurement–feedback control has emerged as a promising route to state preparation, existing schemes either rely on handcr...

Internalizing Personal Mastery through Bī’ah Lughawiyyah and its Contribution to Arabic Language Competence

This study examines how bī’ah lughawiyyah supports the development of personal mastery and how this process contributes to students’ Arabic language competence at Wali Songo Ngabar Islamic Boarding School, Ponorogo. Using a qualitative phenomenological approach, data were collected during 2024–2025 through observation,...