Deep Reinforcement Learning in Action
- Paperback: 384 pages
- Publisher: WOW! eBook; 1st edition (May 12, 2020)
- Language: English
- ISBN-10: 1617295434
- ISBN-13: 978-1617295430
Humans learn best from feedback – we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.
Deep Reinforcement Learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep Reinforcement Learning famously contributed to the success of AlphaGo but that’s not all it can do! More exciting applications wait to be discovered. Let’s get started.
- Structuring problems as Markov Decision Processes
- Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them
- Applying reinforcement learning algorithms to real-world problems
Deep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on direct feedback from their environment. You’ll build networks with the popular PyTorch deep learning framework to explore reinforcement learning algorithms ranging from Deep Q-Networks to Policy Gradients methods to Evolutionary Algorithms. As you go, you’ll apply what you know to hands-on projects like controlling simulated robots, automating stock market trades, and even building a bot to play Go.