About this book
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process.
Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning.
The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used.
- Absorb the core concepts of the reinforcement learning process
- Use advanced topics of deep learning and AI
- Work with Open AI Gym, Open AI, and Python
- Harness reinforcement learning with TensorFlow and Keras using Python
Reinforcement Learning Artificial Intelligence Python TensorFlow Keras Deep Learning Machine Learning
- DOI https://doi.org/10.1007/978-1-4842-3285-9
- Copyright Information Abhishek Nandy and Manisha Biswas 2018
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing
- Print ISBN 978-1-4842-3284-2
- Online ISBN 978-1-4842-3285-9
- Buy this book on publisher's site