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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Problem statements and key decisions

The finance domain is large and complex, so you can easily spend several years learning something new every day. In our example, we’ll just scratch the surface a bit with our RL tools and our problem will be formulated as simply as possible, using price as an observation. We will investigate whether it will be possible for our agent to learn when the best time is to buy one single share and then close the position to maximize the profit. The purpose of this example is to show how flexible the RL model can be and what the first steps are that you usually need to take to apply RL to a real-life use case.

As you already know, to formulate RL problems three things are needed: observation of the environment, possible actions, and a reward system. In previous chapters, all three were already given to us and the internal machinery of the environment was hidden. Now we’re in a different situation, so we need to decide ourselves what our agent will see and what...