Reinforcement Learning with Pytorch

人工智能正在动态地进入我们的生活。它已经广泛可用，我们每天都在使用它-有时甚至不知道它。很快它将成为我们永久的，每天的伴侣。

在AI世界中，我们可以将强化学习放在何处？毫无疑问，这是最有希望和发展最快的技术之一，最终可以将我们引向通用人工智能！我们可以看到多个示例，其中AI可以取得惊人的结果-从在玩游戏时达到超人的水平到解决现实生活中的问题（机器人，医疗保健等）。

毫无疑问，值得了解和理解它！

这就是创建本课程的原因。

我们将讨论多个主题，重点放在最重要和最实用的细节上。我们将从最基本的信息开始，逐步建立我们的理解，最后达到使代理人以类人的方式学习的点-仅从视频输入开始！

重要的是-当然我们需要涵盖一些理论-但我们将主要侧重于实践部分。目的是了解为什么和如何。

h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 7h 14 mn | 8.57 GB
Instructor: Atamai AI Team

Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym

What you’ll learn

Reinforcement Learning basics
Tabular methods
Bellman equation
Q Learning
Deep Reinforcement Learning
Learning from video input

Requirements

Basic python knowledge is needed. AI / Machine Learning / Pytorch basics – nice to have but not fully necessary. Only open source tools will be in use.

Description

UPDATE:

All the code and installation instructions have been updated and verified to work with Pytorch 1.3 !!

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 games

What 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 recommendations

See you in the class!
Who this course is for:

Anyone interested in artificial intelligence, data science, machine learning, deep learning and reinforcement learning.