Deep Reinforcement Learning using python

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 free deep learning a z build neural networks tensorflow course.

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.

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

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.

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.

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

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.

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