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TensorFlow 2 Reinforcement Learning Cookbook

You're reading from TensorFlow 2 Reinforcement Learning Cookbook

Product type Book

Published in Jan 2021

Publisher Packt

ISBN-13 9781838982546

Pages 472 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Palanisamy P

Table of Contents (11) Chapters

Preface

1. Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

2. Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms

3. Chapter 3: Implementing Advanced RL Algorithms

4. Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents

5. Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

6. Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

7. Chapter 7: Deploying Deep RL Agents to the Cloud

8. Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents

9. Chapter 9: Deploying Deep RL Agents on Multiple Platforms

10. Other Books You May Enjoy

Leave a review - let other readers know what you think

Building a stock market trading RL platform using price charts

Human traders look at several indicators on their price monitor in order to vet and identify a potential trade. Can we allow the agents to also visually look at the price candlestick charts to trade stocks instead of providing just a tabular/CSV representation? Yes, we can! This recipe will teach you how to build a visually rich trading environment for your RL agents.

Getting ready

To complete this recipe, make sure you have the latest version. You will need to activate the tf2rl-cookbook Python/conda virtual environment. Make sure that you update the environment so that it matches the latest conda environment specification file (tfrl-cookbook.yml), which can be found in this cookbook's code repository. If the following import statements run without any issues, you are ready to get started:

import os
import random
from typing import Dict
import cv2
import gym
import numpy as np
import pandas as pd
from gym...

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Authors (1)

Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

See other products by Palanisamy P

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