What you would learn in Modern Reinforcement Learning: Deep Q Learning in PyTorch course?
In this comprehensive deep reinforcement course, you will be taught an easily repeatable structure for reading and implementing the research on deep reinforcement. Learn about the original research papers that introduced deep reinforcement learning, also known as deep Q-learning, Double Deep Q learning, and Duplicating Deep Q Learning algorithms. Then, you will learn to implement them using pythonic and short PyTorch code, which could be expanded to incorporate any further deep Q algorithms. The algorithms will be utilized to tackle a range of games using the Open AI gym's Atari library, such as Pong, Breakout, and Bank heist.
You will be taught the essential steps to make the Deep Q Learning algorithms work by learning how to alter OAI Gym's Atari library to comply with the requirements from the initial Deep Q Learning papers. Learn how to:
Repeated actions can reduce the computational cost
Change the scale of the Atari image images on the screen to improve the efficiency
Frames are stacked to give the Deep Q agent a sense of movement
Examine the Deep Q agent's capabilities by using random no-ops to address the model's performance over time.
Clip rewards that allow Deep Q to allow the Deep Q learning agent to expand across Atari games using different scoring scales
Suppose you don't have any prior experience with Deep reinforcement, and reinforcement is not an issue. In the course, you will receive an entire and concise class on the basics of reinforcement learning. The introduction to reinforcement learning can be taught within the context of working on issues in the Frozen Lake environment from the Open AI Gym.
We will be covering:
Markov decision processes
Temporal learning differences
The Q algorithm was the first to be developed.
How can you solve the Bellman equation?
Value functions and value for action functions
Model-free model-free vs. model-based reinforcement learning
Solutions to the dilemma of exploring and exploiting that address the dilemma of exploration and exploration, such as optimistic initial values, and epsilon-greedy actions to select
In addition, you will receive a mini-course on deep learning that uses Python. PyTorch framework. This course is designed for learners familiar with the fundamental concepts of deep learning but not the details or who are familiar with deep learning within a different framework like Tensorflow and Keras. You will be taught how to write a deep neural network using Pytorch and how convolutional neural networks work. The knowledge can be used to create a non-naive Deep Q learning agent to solve the Cartpole problem that is part of The Open AI gym.
Course Content:
- How do you read and implement deep reinforcement learning documents
- How do I program Deep Q learning agents
- How to code Double Deep Q Learn Agents
- How to code dueling Deep Q and Dueling Double Deep Q Learning Agents
- How do you create flexible and modular deep reinforcement software
- How can I automate the tuning of hyperparameters using commands line arguments
Download Modern Reinforcement Learning: Deep Q Learning in PyTorch from below links NOW!
