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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-reinforcement/
课程评论：没有评论
**课程名称：** 高级强化学习（Python）：从DQN到SAC **课程概述：** 本课程是Udemy上最全面的高级强化学习课程。通过Python、PyTorch和PyTorch Lightning，您将深入学习并亲手实现一些最强大的深度强化学习算法。您将从零开始构建能够基于经验解决控制任务的自适应算法。课程还将引导您将这些技术与神经网络和深度学习方法相结合，创造出能够解决决策任务的自适应人工智能代理。 本课程将为您介绍强化学习的最新进展，并为后续系列课程做好准备，以便探索在其他类型任务上表现出色的高级方法。 课程注重培养实践技能。在学习每个方法族最重要的概念后，您将在Jupyter Notebook中从零开始实现其一种或多种算法。 **课程模块：** * **基础回顾：** * 马尔可夫决策过程 (MDP) * Q-Learning * 神经网络简介 * 深度Q-Learning (DQN) * 策略梯度方法 * **高级强化学习：** * PyTorch Lightning * 使用Optuna进行超参数调优 * 用于连续动作空间的深度Q-Learning (归一化优势函数 - NAF) * 深度确定性策略梯度 (DDPG) * 双延迟深度确定性策略梯度 (TD3) * 软Actor-Critic (SAC) * 事后经验回放 (HER)
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.- Refresher: Policy gradient methodsAdvanced Reinforcement Learning:- PyTorch Lightning.- Hyperparameter tuning with Optuna.- Deep Q-Learning for continuous action spaces (Normalized advantage function - NAF).- Deep Deterministic Policy Gradient (DDPG).- Twin Delayed DDPG (TD3).- Soft Actor-Critic (SAC).- Hindsight Experience Replay (HER).
