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
Other Books You May Enjoy

The trading environment

As we have lots of code that is supposed to work with OpenAI Gym, we’ll implement the trading functionality following Gym’s Env class API, which should be familiar to you. Our environment is implemented in the StocksEnv class in the Chapter08/lib/ module. It uses several internal classes to keep its state and encode observations. Let’s first look at the public API class.

class Actions(enum.Enum):
    Skip = 0
    Buy = 1
    Close = 2

We encode all available actions as an enumerator’s fields. We support a very simple set of actions with only three options: do nothing, buy a single share, and close the existing position.

class StocksEnv(gym.Env):
    metadata = {‘render.modes’: [‘human’]}

This metadata field is required the for gym.Env compatibility. We don’t provide render functionality, so you can ignore this.

    def from_dir(cls, data_dir, **kwargs):
        prices = {file: data.load_relative(file) for file in data.price_files(data_dir)}