Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
Index

The trading environment

As we have a lot of code (methods, utility classes in PTAN, and so on) that is supposed to work with OpenAI Gym, we will 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 Chapter10/lib/environ.py module. It uses several internal classes to keep its state and encode observations. Let's first look at the public API class:

import gym
import gym.spaces
from gym.utils import seeding
from gym.envs.registration import EnvSpec
import enum
import numpy as np
from . import data
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': ...