DevelopmentAdded 6 days ago

Deep Reinforcement Learning Projects with Python & PyTorch

Build DQN agents for GridWorld, MountainCar, arcade games and stock trading using Python, PyTorch and advanced Q-network

3.8 / 5.0
142 ratings
9h 8m 29s
On-demand
English
Audio
Riad Almadani • 70,000+ Students
Instructor
Deep Reinforcement Learning Projects with Python & PyTorch100% OFF
  • 9h 8m 29s on-demand video
  • Certificate of Completion
  • Mobile, TV & Desktop Access
  • Full Lifetime Access

What you'll learn

Understand deep reinforcement learning and its applications
Build your own neural network
Implement 5 different reinforcement learning projects
Learn a lot of ways to improve your robot

Course Description

Welcome to Deep Reinforcement Learning using python!

Have you ever asked yourself how smart robots are created?

Reinforcement learning  concerned with creating intelligent robots which is a sub-field of machine learning that achieved impressive results in the recent years where now we can build robots that can beat humans in very hard  games like alpha-go game and chess game.

Deep Reinforcement Learning  means Reinforcement learning  field plus deep learning field where deep learning it is also a a sub-field of machine learning  which uses special algorithms called neural networks.

In this course we will talk about Deep Reinforcement Learning and we will talk about the following things :-


  • Section 1: An Introduction to Deep Reinforcement Learning

    In this section we will study all the fundamentals of deep reinforcement learning . These include Policy , Value function , Q function and neural network.


  • Section 2: Setting up the environment

    In this section we will learn how to create our virtual environment and installing all required packages.


  • Section 3: Grid World Game & Deep Q-Learning

    In this section we will learn how to build our first smart robot to solve Grid World Game.

    Here we will learn how to build and train our neural network and how to make exploration and exploitation.


  • Section 4: Mountain Car game & Deep Q-Learning

    In this section we will try to build a robot to solve Mountain Car game.

    Here we will learn how to build ICM module and RND module to solve  sparse reward problem in Mountain Car game.


  • Section 5: Flappy bird game & Deep Q-learning

    In this section we will learn how to build a smart robot  to solve Flappy bird game.

    Here we will learn how to build many  variants of Q network like dueling Q network , prioritized Q network and 2 steps Q network


  • Section 6: Ms Pacman game & Deep Q-Learning

    In this section we will learn how to build a smart robot  to solve Ms Pacman game.

    Here we will learn how to build another  variants of Q network like noisy Q network , double Q network and n-steps Q network.


  • Section 7:Stock trading & Deep Q-Learning

    In this section we will learn how to build a smart robot  for stock trading.



Who this course is for:

  • Anyone who wants to learn about artificial intelligence and deep learning
  • students & professionals

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