Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Before we jump into the code, some introduction is needed. Our examples are becoming increasingly challenging and complex, which is not surprising, as the complexity of problems we're trying to tackle is also growing. The examples are as simple and concise as possible, but some of the code may be difficult to understand at first.

Another thing to note is performance. Our previous examples for FrozenLake, or CartPole, were not demanding from a performance perspective, as observations were small, neural network parameters were tiny, and shaving off extra milliseconds in the training loop wasn't important. However, from now on, that's not the case anymore. One single observation from the Atari environment is 100k values, which has to be rescaled, converted to floats, and stored in the replay buffer. One extra copy of this data array can cost you training speed, which is not seconds and minutes anymore, but could be hours even on the fastest GPU available. The neural network training...