Deep Reinforcement Learning Projects with Python & PyTorch

About this course

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.<

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