Key Laboratory of Optimization Theory and Applications, School of Mathematical Sciences, China West Normal University, Nanchong, China. Overall, existing literature mainly focuses on smooth ...

If it feels like social platforms suddenly “get” you more than they used to, you’re not imagining it! In 2026, feeds aren’t only reacting to what you click anymore. They’re predicting what you ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

This project presents a comprehensive overview of building a simulation environment in Unity and applying the Proximal Policy Optimization (PPO) algorithm from Unity’s built-in ML-Agents toolkit. We ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

A new algorithm helps topology optimizers skip unnecessary iterations, making optimization and design faster, more stable and more useful. PROVIDENCE, R.I. [Brown University] — With the rise of 3D ...

ABSTRACT: This study introduces a novel simulation-based framework that integrates Agent-Based Modelling (ABM) with Reinforcement Learning (RL) to evaluate and optimize policies for mental health ...

Goal-reaching simulation in Unity by combining to use ML-Agents toolkit and Anaconda involves training an agent to navigate and interact with environments to reach predefined goal target. This task ...

Abstract: Proximal Policy Optimization (PPO), as an outstanding Reinforcement learning (RL) algorithm, has proven its efficiency when solving a wide range of problems. Compared to other reinforcement ...