徐尔瀚，伦敦政治经济学院 (LSE)统计系在读一年级博士，师从史成春教授。主要研究方向包括强化学习，大语言模型的微调与优化。目前主要的研究方向为统计学方法与大预言模型的交叉应用。

First, let's discuss the core elements of this development, with algorithms being the most critical. In AI agent development, we often mention the use of machine learning algorithms, and of course, ...

Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

WiMi's deep reinforcement learning-based task scheduling algorithm in cloud computing includes state representation, action selection, reward function and training and optimization of the algorithm.

Researchers propose a method that allows reinforcement learning algorithms to accumulate knowledge while erring on the side of caution.

CoreWeave said it will acquire OpenPipe, a Bellevue, Wash.-based startup that helps developers train AI agents using ...

Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques.

Neuroscientist Daeyeol Lee discusses different modes of reinforcement learning in humans, animals, and AI, and future directions of research.