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
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Things to try

In this chapter, we've only just started playing with MiniWoB, by touching upon the six easiest environments from the full set of 80 problems, so there is plenty of uncharted territory ahead. If you want to practice, there are several items you can experiment with:

  • Testing the robustness of demonstrations to noisy clicks.

  • Implementing training of the value head of A2C based on demonstration data.

  • Implementing more sophisticated mouse control, like Move mouse N pixels left/right/top/bottom.

  • Using some pretrained OCR net (or train your own!) to extract text information from the observations.

  • Taking other problems and trying to solve them. There are some quite tricky and fun problems, like sort items using drag-n-drop, or repeat the pattern using checkboxes.