This introduction to deep reinforcement learning (RL) combines both theory and implementation. Authors Laura Graesser and Wah Loon Keng starts with intuition, then carefully explain the theory of deep RL algorithms, discuss implementations in its companion software library SLM Lab, and finish with the practical details of getting deep RL to work. This guide is aimed both at computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.

<ASIN:0135172381>

Author: Laura Graesser and Wah Loon Keng
Publisher: Addison-Wesley
Date: December 2019
Pages: 416
ISBN: 978-0135172384
Print: 0135172381
Kindle: B07ZVYZC6F
Audience: Developers in machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

• 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

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