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Cutting-Edge AI: Deep Reinforcement Learning in Python Course

Cutting-Edge AI: Deep Reinforcement Learning in Python Course
Cutting-Edge AI: Deep Reinforcement Learning in Python Course

Cutting-Edge AI: Deep Reinforcement Learning in Python Course

Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG

What you’ll learn

Cutting-Edge AI: Deep Reinforcement Learning in Python Course

  • Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)
  • Learn How to implement Evolution Strategies (ES) for AI
  • Understand and implement DDPG (Deep Deterministic Policy Gradient)

Requirements

  • Know the basics of MDPs (Markov Decision Processes) and Reinforcement Learning
  • Helpful to have seen my first two Reinforcement Learning courses
  • Know how to build a convolutional neural network in Tensorflow

Description

This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.

Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).

Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.

We’ve seen how AlphaZero can master the game of Go using only self-play.

This is just a few years after the original AlphaGo already beat a world champion in Go.

If your agent falls down, no real damage is done.

We’ve seen real-world robots learn hand dexterity, which is no small feat.
[adss] Hand dexterity is complex – you have many degrees of freedom and many of the forces involved are extremely subtle.

Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult.

Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2.

This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.

Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more “black box” approach, inspired by biological evolution.
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First, we’re going to look at the classic Atari environments.

Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics – we first have to show it can work with simulated physics.

Thanks for reading, and I’ll see you in class!

Suggested prerequisites:

  • Calculus
  • Probability
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations
  • Linear regression
  • Gradient descent
  • Know how to build a convolutional neural network (CNN) in TensorFlow
  • Markov Decision Processes (MDPs)

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don’t, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

  • Students and professionals who want to apply Reinforcement Learning to their work and projects
  • Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms
  • Content From: https://www.udemy.com/cutting-edge-artificial-intelligence/
  • Learn Python Basics Course

Cutting-Edge AI: Deep Reinforcement Learning in Python Course

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