Biologically plausible learning now reaches 96.7% on MNIST and 61.7% on CIFAR-10 without backpropagation, as Sakana AI ...

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.

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: Accurate prediction of stock prices remains a fundamental challenge in financial markets, with substantial implications for investment strategies and decision making. Although machine ...

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 ...