How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

What you'll learn
How to read and implement deep reinforcement learning papers
How to code Deep Q learning agents
How to Code Double Deep Q Learning Agents
How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents
How to write modular and extensible deep reinforcement learning software
How to automate hyperparameter tuning with command line arguments

Requirements
Some College Calculus
Exposure To Deep Learning
Comfortable with Python

Description
In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch and Tensorflow 2 code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist. You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:Repeat actions to reduce computational overheadRescale the Atari screen images to increase efficiencyStack frames to give the Deep Q agent a sense of motionEvaluate the Deep Q agent's performance with random no-ops to deal with model over trainingClip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scalesIf you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym. We will cover:Markov decision processesTemporal difference learningThe original Q learning algorithmHow to solve the Bellman equationValue functions and action value functionsModel free vs. model based reinforcement learningSolutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selectionAlso included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym.

Who this course is for:
Python developers eager to learn about cutting edge deep reinforcement learning

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