|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-rl-pg/
课程评论：没有评论
**课程名称：** 高级强化学习：策略梯度方法 **课程概要：** 本课程是 Udemy 上最全面的强化学习课程系列之一。您将使用 Python、PyTorch 和 PyTorch Lightning 实现一些最强大的深度强化学习算法。您将从头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，创建能够解决决策任务的自适应人工智能代理。 课程将介绍强化学习的最新技术，并为后续课程做好准备，后续课程将探索在其他类型任务中表现出色的高级方法。 本课程注重实践技能的培养。在学习各家族方法的核心概念后，我们将从头在 Jupyter Notebook 中实现一种或多种算法。 **学习模块：** **基础回顾：** * 马尔可夫决策过程 (MDP) * 蒙特卡洛方法 * 时间差分方法 * N步自举 * 神经网络简述 * 策略梯度方法 **高级强化学习：** * REINFORCE * REINFORCE 连续动作空间 * 优势演员-评论家 (A2C) * 信任区域方法 * 近端策略优化 (PPO) * 广义优势估计 (GAE) * 信任区域策略优化 (TRPO)
This is the most complete Reinforcement Learning course series 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: Monte Carlo methods.- Refresher: Temporal difference methods.- Refresher: N-step bootstrapping.- Refresher: Brief introduction to Neural Networks.- Refresher: Policy gradient methods.Advanced Reinforcement Learning:- REINFORCE- REINFORCE for continuous action spaces- Advantage actor-critic (A2C)- Trust region methods- Proximal policy optimization (PPO)- Generalized advantage estimation (GAE)- Trust region policy optimization (TRPO)
