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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The Q-learning method that we've just seen solves the issue with iteration over the full set of states, but still can struggle with situations when the count of the observable set of states is very large. For example, Atari games can have a large variety of different screens, so if we decide to use raw pixels as individual states, we'll quickly realize that we have too many states to track and approximate values for.

In some environments, the count of different observable states could be almost infinite. For example, in CartPole the state given to us by the environment is four floating point numbers. The number of combinations of values is finite (they're represented as bits), but this number is extremely large. We could create some bins to discretize those values, but this often creates more problems than it solves: we would need to decide what ranges of parameters are important to distinguish as different states and what ranges could be clustered together.

In the case of...