|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/reinforcement-learning-with-pytorch/
课程评论：没有评论
课程名称：使用 PyTorch 进行强化学习 概述：本课程旨在帮助学员掌握强化学习的基础知识和实用技巧，助力人工智能技术的理解与应用。随着人工智能技术的迅速发展，强化学习已经成为AI领域中最具前景和增长速度最快的技术之一，有望引导我们走向通用人工智能。课程通过一系列主题深入探索强化学习的基本概念，包括从基础知识到复杂应用的进阶理解。 课程内容包括： - 强化学习简介 - 马尔可夫决策过程 - 确定性和随机性环境 - 贝尔曼方程 - Q学习 - 探索与利用的平衡 - 可扩展性 - 神经网络作为函数逼近器 - 深度强化学习 - 深度Q网络（DQN） - 对DQN的改进 - 从视频输入学习 - 复现一些流行的强化学习解决方案 - 参数调节和一般建议 通过使用 OpenAI Gym 提供的环境，本课程将从基础文本游戏开始，逐步提升到复杂的游戏，例如挑战性的Atari游戏。尽管理论部分是课程的一部分，我们将更侧重于实践，以帮助学员理解“为什么”和“如何”。希望在课堂上见到你！
UPDATE:All the code and installation instructions have been updated and verified to work with Pytorch 1.6!!Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it - sometimes even not knowing it - on daily basis. Soon it will be our permanent, every day companion.And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence! We can see multiple examples where AI can achieve amazing results - from reaching super human level while playing games to solving real life problems (robotics, healthcare, etc).Without a doubt it's worth to know and understand it!And that's why this course has been created.We will go through multiple topics, focusing on most important and practical details. We will start from very basic information, gradually building our understanding, and finally reaching the point where we will make our agent learn in human-like way - only from video input!What's important - of course we need to cover some theory - but we will mainly focus on practical part. Goal is to understand WHY and HOW.In order to evaluate our algorithms we will use environments from - very popular - OpenAI Gym. We will start from basic text games, through more complex ones, up to challenging Atari gamesWhat will be covered during the course ? - Introduction to Reinforcement Learning- Markov Decision Process- Deterministic and stochastic environments- Bellman Equation- Q Learning- Exploration vs Exploitation- Scaling up- Neural Networks as function approximators- Deep Reinforcement Learning- DQN- Improvements to DQN- Learning from video input- Reproducing some of most popular RL solutions- Tuning parameters and general recommendationsSee you in the class!
