Abstract: This paper presents a simulation-based benchmarking analysis of three reinforcement learning (RL) algorithms—Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Here Are the States That Won't Tax ...

This project implements various reinforcement learning algorithms to play Spider Solitaire, a popular card game. The implementation includes DQN, A2C, and PPO algorithms with both full and simplified ...

This GitHub repository contains the code, data, and figures for the paper FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models. Also includes ...

Spotware, the developer of the cTrader multi-asset trading platform has launched an essential update with the introduction of cTrader Windows version 5.4, native Python, supporting algorithmic trading ...

Spotware, the developer of the cTrader multi-asset trading platform has launched an essential update with the introduction of cTrader Windows version 5.4, native Python, supporting algorithmic trading ...