Hands On Reinforcement Learning with Python[Video], Published by Packt
This is the code repository for Hands - On Reinforcement Learning with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
The scope of Reinforcement Learning applications outside toy examples is immense. Reinforcement Learning can optimize agricultural yield in IoT powered greenhouses, and reduce power consumption in data centers. It's grown in demand to the point where its applications range from controlling robots to extracting insights from images and natural language data. By the end of this course, you will not only be able to solve these problems but will also be able to use Reinforcement Learning as a problem-solving strategy and use different algorithms to solve these problems.
- Spot new opportunities to deploy RL by mastering its core concepts and real-life examples
- Learn to identify RL problems by creating a multi-armed bandit environment in Python
- Deploy the Swiss-army-knife of RL by solving multi-armed and contextual bandit problems
- Optimize for long-term rewards by implementing a dynamically programmed agent
- Plugin a Neural Network into your software agent to learn complex interactions
- Combine the advantages of both Monte Carlo and dynamic programming in SARSA
To fully benefit from the coverage included in this course, you will need:
This course is intended for people who have some understanding of supervised learning, and are interested in artificial intelligence. After completing the course, you can delve into specific RL topics, and start solving more complex RL problems on OpenAI Gym or similar. No prior Reinforcement Learning knowledge is required, although knowing Python and having a quantitative background will help you follow the video more effectively.
This course has the following software requirements:
This will vary on a product-by-product basis, but should be a standard PI element for ILT products. This example is relatively basic.
Minimum Hardware Requirements For successful completion of this course, students will require the computer systems with at least the following:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Processor: Intel Core i5 or equivalent
Memory: 8 GB RAM
Storage: 35 GB available space
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Processor: Intel Core i7 or equivalent
Memory: 48 GB RAM
Storage: 35 GB available space
Software Requirements OS: Windows 7 or Windows 10
Browser: Google Chrome, Latest Version
Code Editor: Atom IDE, Latest Version
Others: Python3 installed using the Anaconda package or equivalent, Tensorflow r1.4