Foundations of Deep Reinforcement Learning: Theory and Practice in Python

by Laura Graesser, Wah Loon Keng

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Genres: Artificial Intelligence

416 pages, Kindle Edition

Published November 20, 2019

Reviews

[eri b.❀ · Rating 4 out of 5]

Bit of a disclaimer because I only read some selected chapters, but overall the algorithms were well-explained, though I would have liked more discussion about the implementations, had to dig through code to find some explanations. Still very comprehensive in general.

[Adrián Domínguez · Rating 5 out of 5]

You must have prior knowledge about reinforcement learning basics and about neural networks to be able to understand the algorithm explanations. With that knowledge, this books makes a great job explaining RL algorithms and part of the mathematics that are behind them. It also includes lots of source code implementing the algorithms that are mostly well explained, although the read must know PyTorch to be able to understand everything clearly. I think it's a great book but for an intermediate level, not to start with RL or neural networks.

[Alan Couzens · Rating 5 out of 5]

The most comprehensive book on Deep Reinforcement Learning that I have read. Provides a great introduction to the topic & covers all algorithms that fall under the DRL domain clearly. Would recommend.