Deep Reinforcement Learning using python

• Understand deep reinforcement learning and its applications
• Build your own neural network
• Implement 5 different reinforcement learning projects 

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.