reinforcement learning

Selectivity Dilution in Three-Factor Learning: A Finite-Population Model with Potential Implications for Motor Development and Post-Stroke Motor Rehabilitation

Motor development and post-stroke motor recovery may share underlying learning mechanisms, but the early mechanical environments of the two processes differ strikingly. During early development the fetus moves within amniotic fluid, where buoyancy substantially reduces gravitational load and enables spontaneous low-res...

Comment on wes-2026-157

Abstract. To address the difficulty in coordinating component-degradation regulation strategies with opportunistic maintenance timings over the full lifecycle of wind turbines, this study proposes a reinforcement-learning–based collaborative optimization method for wind-turbine load regulation and opportunistic mainten...

ALGORITHMIC JUSTICE FROM RULE OF LAW TO RULE OF CODE The El-Rakhawi Doctrine of Computational Adjudication From Human Jurisprudence to Self-Evolving Legal Systems - A Unified Framework for the Age of Autonomous Adjudication, Algorithmic Bias as Crime, and AGI Accountability

ALGORITHMIC JUSTICE by Dr. M.K.A. El-Rakhawi (DOI: 10.5281/zenodo.23499006) establishes a unified framework for computational adjudication across seven parts. It transitions from Rule of Law to Rule of Code, recognizing smart contracts as self-executing law and DAOs as algorithmic legal persons. Algorithmic bias is for...

AI in the Game Industry: Latest Research Advances and Future Direction

The paper is a summary of the current developments of AI in the gaming sector between 2023 and 2026, including automated generation of content, intelligent game agents (NPCs) and large language models (LLMs), game testing and quality assurance by means of AI, modeling of players and dynamic difficulty management, as we...

Research on Path Planning Method Based on Deep Reinforcement Learning for Autonomous Driving

This article introduces a mixed deep reinforcement learning path planning framework (DRL-PP) for self-driving cars. It's designed to be efficient, safe, and verifiable in complex, dynamic settings. The framework is built on layered action representation: the policy network gives out multi-step reference waypoints, and...

Ranked behavior cloning for productivity-driven imitation learning with zone-based safety modulation in human–robot collaborative tasks

A Surrogate-Assisted Deep Reinforcement Learning Framework for Gear Center Distance Optimization in Machine Tool Assembly

New hybrid models integrating the aquila optimizer with machine learning algorithms to predict the strength of geosynthetic-reinforced subgrade soil

Accurately predicting the California Bearing Ratio (CBR) of geosynthetic-reinforced subgrades is critical for pavement design but remains challenging due to the complex, nonlinear soil-reinforcement interaction. This study develops a novel hybrid modeling framework that integrates the Aquila Optimization Algorithm (AOA...

Chapter 43: 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...