11.11.2022, 14:59
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Reinforcement Learning With Pytorch
Last updated 8/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.56 GB | Duration: 7h 13m
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 1.6 !!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!
Overview
Section 1: Welcome to the course
Lecture 1 Welcome!
Lecture 2 Before you start - Videos quality!
Lecture 3 Resources
Section 2: Introduction
Lecture 4 Introduction #1
Lecture 5 Introduction #2
Lecture 6 Introduction #3
Lecture 7 Introduction #4
Lecture 8 Environment setup / Installation
Lecture 9 Lab. OpenAI Gym #1
Lecture 10 Lab. OpenAI Gym #2
Lecture 11 Lab. OpenAI Gym #3
Lecture 12 Lab. OpenAI Gym #4
Section 3: Tabular methods
Lecture 13 Deterministic & Stochastic environments
Lecture 14 Rewards
Lecture 15 Bellman equation #1
Lecture 16 Bellman equation #2
Lecture 17 Resource - code
Lecture 18 Lab. Algorithm for deterministic environments #1
Lecture 19 Lab. Algorithm for deterministic environments #2
Lecture 20 Lab. Algorithm for deterministic environments #3
Lecture 21 Lab. Algorithm for deterministic environments #4
Lecture 22 Lab. Test with stochastic environment
Lecture 23 Q-Learning
Lecture 24 Lab. Algorithm for stochastic environments
Lecture 25 Exploration vs Exploitation
Lecture 26 Lab. Egreedy
Lecture 27 Lab. Adaptive egreedy
Lecture 28 Bonus Lab. Value iteration
Lecture 29 Homework
Lecture 30 Homework. Solution
Lecture 31 Homework. Tuning
Section 4: Scaling up
Lecture 32 Scaling up
Lecture 33 Neural Networks review
Lecture 34 Lab. Neural Networks review #1
Lecture 35 Lab. Neural Networks review #2
Lecture 36 Lab. Random CartPole
Lecture 37 Lab. Epsilon egreedy revisited
Lecture 38 Lab. Pytorch updated ( version 0.4.0 )
Lecture 39 Article. Pytorch updated! (further versions)
Lecture 40 Lab. OpenAI Gym + Neural Network #1
Lecture 41 Lab. OpenAI Gym + Neural Network #2
Lecture 42 Lab. OpenAI Gym + Neural Network #3
Lecture 43 Lab. Extended logging
Section 5: DQN
Lecture 44 Deep Reinforcement Learning
Lecture 45 Lab. Deep Reinforcement Learning
Lecture 46 Lab. Tuning challenge
Lecture 47 Experience Replay
Lecture 48 Lab. Experience Replay #1
Lecture 49 Lab. Experience Replay #2
Lecture 50 Lab. Experience Replay #3
Lecture 51 DQN
Lecture 52 Lab. DQN
Section 6: DQN Improvements
Lecture 53 Double DQN
Lecture 54 Lab. Double DQN
Lecture 55 Dueling DQN
Lecture 56 Lab. Dueling DQN
Lecture 57 Lab. Dueling DQN Challenge
Section 7: DQN with video output
Lecture 58 CNN Review
Lecture 59 Lab. Random Pong
Lecture 60 Saving & Loading the Model
Lecture 61 Lab. Pong from video output #1
Lecture 62 Lab. Pong from video output #2
Lecture 63 Lab. Pong from video output #3
Lecture 64 Lab. Pong from video output #4
Lecture 65 Lab. Pong from video output #5
Lecture 66 Lab. Pong from video output #6
Lecture 67 Potential improvements
Lecture 68 Article. Stacking 4 images together
Section 8: Final notes
Lecture 69 What's next?
Anyone interested in artificial intelligence, data science, machine learning, deep learning and reinforcement learning.
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