Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

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 functionsM