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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

Preface 1. What is Reinforcement Learning? FREE CHAPTER 2. OpenAI Gym 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. DQN Extensions 8. Stocks Trading Using RL 9. Policy Gradients – An Alternative 10. The Actor-Critic Method 11. Asynchronous Advantage Actor-Critic 12. Chatbots Training with RL 13. Web Navigation 14. Continuous Action Space 15. Trust Regions – TRPO, PPO, and ACKTR 16. Black-Box Optimization in RL 17. Beyond Model-Free – Imagination 18. AlphaGo Zero Other Books You May Enjoy Index

Trading

There are lots 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 which is changed over time. Trading is an activity of buying and selling financial instruments with different goals, like making profit (investment), gaining protection from future price movement (hedging) or just getting what you need (like buying steel for your manufacture or exchanging USD to JPY to pay a contract).

Since the first financial market was established, people have been trying to predict future price movements, as this promises lots of benefits, like “profit from nowhere” or protecting...

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