Graesser, Laura Harding
Deep Reinforcement Learning in Python: A Hands-On Introduction
- ISBN 13:
- 9780135172384
- author:
- Graesser, Laura Harding
- format:
- Paperback / softback
- publisher:
- Addison-Wesley Educational Publishers Inc
- language:
- English
- Publication Year:
- 2020
- Pages:
- 360
- Dimensions:
- 23.2 x 17.8 centimetres
- Genre:
- Computers, Server & Database, General Database,
- Condition:
- New
- Availability:
- Item usually sent within 2 working days
Description
Deep reinforcement learning systems have achieved remarkable results in just a few years, thanks to their hybrid approach that shares similarities with human learning. This method combines unsupervised self-learning, self-discovery of strategies, and the use of memory, while balancing exploration and exploitation. The authors provide a practical introduction to deep reinforcement learning using Python, focusing on hands-on examples presented through their advanced OpenAI Lab framework. The book covers key topics such as components of an RL system, value-based algorithms, policy-based algorithms, combined methods, agent evaluation, and advanced techniques. By combining deep neural networks with reinforcement learning, readers can achieve breakthrough machine learning performance. With its well-designed code examples, complete experimental data sets, and practical tips, this book helps reduce the learning curve by relying on the authors' OpenAI Lab framework. It prepares readers for exciting future advances in artificial general intelligence.
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