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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In the previous section, we discussed the REINFORCE method, which is a natural extension of cross-entropy from Chapter 4, The Cross-Entropy Method. Unfortunately, both REINFORCE and cross-entropy still suffer from several problems, which make both of them limited to simple environments.

Full episodes are required

First of all, we still need to wait for the full episode to complete before we can start training. Even worse, both REINFORCE and cross-entropy behave better with more episodes used for training (just from the fact that more episodes mean more training data, which means more accurate PG). This situation is fine for short episodes in the CartPole, when in the beginning, we can barely handle the bar for more than 10 steps, but in Pong, it is completely different: every episode can lasts hundreds or even thousands of frames. It’s equally bad from the training perspective, as our training batch becomes very large and from sample efficiency, when we need to communicate...