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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-deep-qnetworks/
课程评论：没有评论
**课程名称:** 高级Python强化学习：前沿DQN算法 **课程概述:** 本课程是Udemy上最全面的高级强化学习课程之一。您将学习如何使用PyTorch和PyTorch Lightning在Python中实现最强大的深度强化学习算法。您将从零开始实现解决基于经验的控制任务的自适应算法。您还将学习如何将这些技术与神经网络和深度学习方法相结合，以创建能够解决决策任务的自适应人工智能代理。 本课程旨在介绍强化学习技术的最新进展，并为后续系列课程做准备，在后续课程中将探索其他在不同任务类型中表现出色的高级方法。 课程注重培养实践技能。因此，在学习每个方法族最重要的概念后，我们将从零开始在Jupyter Notebook中实现其一种或多种算法。 **课程模块:** * **复习模块:** * 马尔可夫决策过程（MDP） * Q-Learning * 神经网络简介 * 深度Q-Learning * **高级强化学习模块:** * PyTorch Lightning * 使用Optuna进行超参数调优 * 基于图像输入的强化学习 * Double Deep Q-Learning * Dueling Deep Q-Networks * Prioritized Experience Replay (PER) * Distributional Deep Q-Networks * Noisy Deep Q-Networks * N-step Deep Q-Learning * Rainbow Deep Q-Learning
This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.Leveling modules: - Refresher: The Markov decision process (MDP).- Refresher: Q-Learning.- Refresher: Brief introduction to Neural Networks.- Refresher: Deep Q-Learning.Advanced Reinforcement Learning:- PyTorch Lightning.- Hyperparameter tuning with Optuna.- Reinforcement Learning with image inputs- Double Deep Q-Learning- Dueling Deep Q-Networks- Prioritized Experience Replay (PER)- Distributional Deep Q-Networks- Noisy Deep Q-Networks- N-step Deep Q-Learning- Rainbow Deep Q-Learning
