Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
7 Hour(s) 14 Minute(s)
Language
English
Taught by
Atamai AI Team
Rating
2 views
Course Overview
Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym
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!
Course Content
- 8 section(s)
- 69 lecture(s)
- Section 1 Welcome to the course
- Section 2 Introduction
- Section 3 Tabular methods
- Section 4 Scaling up
- Section 5 DQN
- Section 6 DQN Improvements
- Section 7 DQN with video output
- Section 8 Final notes
What You’ll Learn
- Reinforcement Learning basics
- Tabular methods
- Bellman equation
- Q Learning
- Deep Reinforcement Learning
- Learning from video input
Reviews
-
GGiorgio Bonacorsi
Amazing course!!!
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PPablo Martínez-Agulló
- Overall Quality: Excellent for beginners, with a clear and engaging instructor who makes the material easy to follow. - Content: The foundational concepts are well-explained. However, imo, some mathematical details could be expanded for a more in-depth understanding. - Relevance: The course content, particularly the reviewed papers, are a bit outdated (~2015). An updated syllabus with more recent research would significantly enhance its value. - Coding: While the code is functional and serves its purpose, the style and best practices are lacking. I've re-written the scripts with modern formatting and linting standards (Black, isort, Ruff, Pylint, MyPy) for improved readability and maintainability, available at: https://github.com/MartinezAgullo/rl-exercises-gymnasium/
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IIvan Law
Easy to follow. Good illustration examples.
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CCharles Rongione
Thank you so much for this wonderfull course. The instructor was very clear. I particularly appreciated that he coded everything from scratch and show the papers.
