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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A2C baseline

To establish the baseline results, we'll use the A2C method, in a very similar way to the code in the previous chapter. The complete source is in files Chapter15/ and Chapter15/lib/ There are a few differences between this baseline and version we've used in the previous chapter. First of all, there are 16 parallel environments used to gather the experience during the training. The second difference is the model structure and the way that we perform exploration. To illustrate them, let's look at the model and the agent classes.

Both the actor and critic are placed in the separate networks without sharing weights. They follow the approach used in the previous chapter, when our critic estimates the mean and the variance for the actions, but now, variance is not a separate head of the base network, but just a single parameter of the model. This parameter will be adjusted during the training by SGD, but it doesn't depend on the observation.