Reinforcement Learning with Pytorch

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Atamai AI Team

7:10:44

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1 - Welcome to the course

1 - Welcome.mp4

02:22

2 - Before you start Videos quality.html

3 - RLinPractice.zip

3 - ReinforcementLearningWithPytorch.zip

3 - Reinforcement-Learning-with-Pytorch.pdf

3 - Resources.html

2 - Introduction

4 - Introduction 1.mp4

04:28

5 - Introduction 2.mp4

05:09

6 - Introduction 3.mp4

04:33

7 - Introduction 4.mp4

06:00

8 - Environment setup Installation.mp4

01:14

9 - Lab OpenAI Gym 1.mp4

03:06

10 - Lab OpenAI Gym 2.mp4

10:40

11 - Lab OpenAI Gym 3.mp4

02:57

12 - Lab OpenAI Gym 4.mp4

06:00

3 - Tabular methods

13 - Deterministic & Stochastic environments.mp4

07:28

14 - Rewards.mp4

04:31

15 - Bellman equation 1.mp4

06:11

16 - Bellman equation 2.mp4

02:57

17 - Resource code.html

18 - Lab Algorithm for deterministic environments 1.mp4

10:23

19 - Lab Algorithm for deterministic environments 2.mp4

10:45

20 - Lab Algorithm for deterministic environments 3.mp4

10:34

21 - Lab Algorithm for deterministic environments 4.mp4

06:24

22 - Lab Test with stochastic environment.mp4

04:04

23 - QLearning.mp4

10:08

24 - Lab Algorithm for stochastic environments.mp4

06:50

25 - Exploration vs Exploitation.mp4

02:56

26 - Lab Egreedy.mp4

07:34

27 - Lab Adaptive egreedy.mp4

05:39

28 - Bonus Lab Value iteration.mp4

11:32

29 - Homework.mp4

04:30

30 - Homework Solution.mp4

05:17

31 - Homework Tuning.mp4

10:23

4 - Scaling up

32 - Scaling up.mp4

06:04

33 - Neural Networks review.mp4

04:00

34 - Lab Neural Networks review 1.mp4

09:51

35 - Lab Neural Networks review 2.mp4

10:08

36 - Lab Random CartPole.mp4

06:45

37 - Lab Epsilon egreedy revisited.mp4

02:37

38 - Lab Pytorch updated version 040.mp4

07:35

39 - Article Pytorch updated further versions.html

40 - Lab OpenAI Gym Neural Network 1.mp4

10:04

41 - Lab OpenAI Gym Neural Network 2.mp4

08:53

42 - Lab OpenAI Gym Neural Network 3.mp4

04:35

43 - Lab Extended logging.mp4

10:18

5 - DQN

44 - Deep Reinforcement Learning.mp4

03:55

45 - Lab Deep Reinforcement Learning.mp4

05:48

46 - Lab Tuning challenge.mp4

01:39

47 - Experience Replay.mp4

06:14

48 - Lab Experience Replay 1.mp4

10:05

49 - Lab Experience Replay 2.mp4

09:25

50 - Lab Experience Replay 3.mp4

05:06

51 - DQN.mp4

07:26

52 - Lab DQN.mp4

09:12

6 - DQN Improvements

53 - Double DQN.mp4

07:31

54 - Lab Double DQN.mp4

07:30

55 - Dueling DQN.mp4

06:33

56 - Lab Dueling DQN.mp4

07:46

57 - Lab Dueling DQN Challenge.mp4

05:55

7 - DQN with video output

58 - CNN Review.mp4

05:53

59 - Lab Random Pong.mp4

08:32

60 - Saving & Loading the Model.mp4

01:19

61 - Lab Pong from video output 1.mp4

09:53

62 - Lab Pong from video output 2.mp4

09:01

63 - Lab Pong from video output 3.mp4

10:10

64 - Lab Pong from video output 4.mp4

10:13

65 - Lab Pong from video output 5.mp4

09:05

66 - Lab Pong from video output 6.mp4

10:34

67 - Potential improvements.mp4

04:09

68 - Article Stacking 4 images together.html

8 - Final notes

69 - Whats next.mp4

02:25

Description

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

Who is this for?

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

What You Need to Know?

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.

More details

Description
UPDATE:
All the code and installation instructions have been updated and verified to work with Pytorch 1.6 !!

Artificial IntelligenceÂ is dynamicallyÂ edging its wayÂ into our lives. It is alreadyÂ broadly available andÂ we use it - sometimes evenÂ not knowing itÂ -Â on daily basis. Soon it will be our permanent, every dayÂ companion.
And whereÂ can we place ReinforcementÂ Learning in AIÂ world? DefinitelyÂ this is one of the most promising and fastest growing technologies that can eventuallyÂ lead us to General Artificial Intelligence! We can see multiple examples where AIÂ can achieve amazing results - from reachingÂ super human level whileÂ playing gamesÂ toÂ solvingÂ real life problems (robotics, healthcare, etc).
Without a doubtÂ it's worth to knowÂ and understandÂ it!
And that's why this course has been created.
We will go through multiple topics, focusing on most important and practical details.Â We will start from very basic information, graduallyÂ building our understanding, and finallyÂ reaching the point whereÂ we will make our agent learn in human-like way - only from video input!
What's important - of courseÂ weÂ need to cover some theory - but we will mainly focus on practical part. Goal isÂ to understand WHYÂ and HOW.
In order to evaluate our algorithms we willÂ useÂ environments from - very popular -Â OpenAIÂ Gym. We will start from basic text games, through more complex ones, up to challenging Atari games
WhatÂ will be covered during the course ?Â
- 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 andÂ general recommendations
See you in theÂ class!
Who this course is for:
Anyone interested in artificial intelligence, data science, machine learning, deep learning and reinforcement learning.

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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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