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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan

Table of Contents (28) Chapters

Preface What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL Libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients – an Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld Environment Web Navigation Continuous Action Space RL in Robotics Trust Regions – PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced Exploration Beyond Model-Free – Imagination AlphaGo Zero RL in Discrete Optimization Multi-agent RL Other Books You May Enjoy
Index

Trading

There are a lot of financial instruments traded on markets every day: goods, stocks, and currencies. Even weather forecasts can be bought or sold using so-called "weather derivatives," which is just a consequence of the complexity of the modern world and financial markets. If your income depends on future weather conditions, like a business growing crops, then you might want to hedge the risks by buying weather derivatives. All these different items have a price that changes over time. Trading is the activity of buying and selling financial instruments with different goals, like making a profit (investment), gaining protection from future price movement (hedging), or just getting what you need (like buying steel or exchanging USD for JPY to pay a contract).

Since the first financial market was established, people have been trying to predict future price movements, as this promises many benefits, like "profit from nowhere" or protecting capital from sudden...

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