h264, yuv420p, 1280x720 |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.