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Development Data Science Reinforcement Learning

Reinforcement Learning with Pytorch

Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym
Rating: 4.1 out of 54.1 (322 ratings)
2,250 students
Created by Atamai AI Team
Last updated 8/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

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

Course content

8 sections • 69 lectures • 7h 14m total length

  • Preview02:22
  • Before you start - Videos quality!
    00:45
  • Resources
    01:07

  • Preview04:28
  • Introduction #2
    05:09
  • Introduction #3
    04:33
  • Introduction #4
    06:00
  • Preview01:14
  • Lab. OpenAI Gym #1
    03:06
  • Lab. OpenAI Gym #2
    10:40
  • Lab. OpenAI Gym #3
    02:57
  • Lab. OpenAI Gym #4
    06:00

  • Deterministic & Stochastic environments
    07:28
  • Rewards
    04:31
  • Bellman equation #1
    06:11
  • Bellman equation #2
    02:57
  • Resource - code
    00:21
  • Lab. Algorithm for deterministic environments #1
    10:23
  • Lab. Algorithm for deterministic environments #2
    10:45
  • Lab. Algorithm for deterministic environments #3
    10:34
  • Lab. Algorithm for deterministic environments #4
    06:24
  • Lab. Test with stochastic environment
    04:04
  • Q-Learning
    10:08
  • Lab. Algorithm for stochastic environments
    06:50
  • Exploration vs Exploitation
    02:56
  • Lab. Egreedy
    07:34
  • Lab. Adaptive egreedy
    05:39
  • Bonus Lab. Value iteration
    11:32
  • Homework
    04:30
  • Homework. Solution
    05:17
  • Homework. Tuning
    10:23

  • Scaling up
    06:04
  • Neural Networks review
    04:00
  • Lab. Neural Networks review #1
    09:51
  • Lab. Neural Networks review #2
    10:08
  • Lab. Random CartPole
    06:45
  • Lab. Epsilon egreedy revisited
    02:37
  • Lab. Pytorch updated ( version 0.4.0 )
    07:35
  • Article. Pytorch updated! (further versions)
    00:10
  • Lab. OpenAI Gym + Neural Network #1
    10:04
  • Lab. OpenAI Gym + Neural Network #2
    08:53
  • Lab. OpenAI Gym + Neural Network #3
    04:35
  • Lab. Extended logging
    10:18

  • Deep Reinforcement Learning
    03:55
  • Lab. Deep Reinforcement Learning
    05:48
  • Lab. Tuning challenge
    01:39
  • Preview06:14
  • Lab. Experience Replay #1
    10:05
  • Lab. Experience Replay #2
    09:25
  • Lab. Experience Replay #3
    05:06
  • DQN
    07:26
  • Lab. DQN
    09:11

  • Double DQN
    07:31
  • Lab. Double DQN
    07:30
  • Dueling DQN
    06:33
  • Lab. Dueling DQN
    07:46
  • Lab. Dueling DQN Challenge
    05:55

  • CNN Review
    05:53
  • Lab. Random Pong
    08:32
  • Saving & Loading the Model
    01:19
  • Lab. Pong from video output #1
    09:53
  • Lab. Pong from video output #2
    09:01
  • Lab. Pong from video output #3
    10:10
  • Lab. Pong from video output #4
    10:13
  • Lab. Pong from video output #5
    09:05
  • Lab. Pong from video output #6
    10:34
  • Potential improvements
    04:09
  • Article. Stacking 4 images together
    01:11

  • What's next?
    02:25

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 1.6 !!


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 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.

Featured review

Clive DaSilva
Clive DaSilva
85 courses
43 reviews
Rating: 5.0 out of 5a year ago
Quite good, excellent text and exercises. I have one question, my Linux computer running Ubuntu 18.04 LTS does not have a gpu so it choked on lesson 68. I copied the code into juypter notebook on Colaboratory and included the atari_wrapper.py in the same folder. The program did not read this file as it did on Spyder. Is there any workaround for this, as I will still be experimenting with the exercises. Thanks and a great course

Instructor

Atamai AI Team
Data Science & AI Passion
Atamai AI Team
  • 4.1 Instructor Rating
  • 322 Reviews
  • 2,250 Students
  • 1 Course

We are independent AI researchers, working with Artificial Intelligence and Deep Learning projects on daily basis. We are absolutely passionate about it and we want to share this passion with you.

We're also experienced instructors (mainly doing in person trainings so far) and we simply love sharing our knowledge with others!

We're looking forward to see you in one of our courses!

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