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

Here is a list of things you can do to improve your understanding of the topic:

  1. In the D4PG code, I used a simple replay buffer, which was enough to get good an improvement over DDPG. You can try to switch the example to the prioritized replay buffer in the same way as we did in Chapter 7, DQN Extensions, and check the effect.

  2. There are lots of interesting and challenging environments around. For example, you can start with other PyBullet environments, but there is also DeepMind Control Suite (there was a paper about it published at the beginning of 2018, comparing the A3C, DDPG, and D4PG methods), MuJoCo-based environments in Gym and lots of others.

  3. You can request the trial license of MuJoCo and compare its stability, performance and resulting policy with PyBullet.

  4. Play with the very challenging Learning how to run competition from NIPS-2017, where you are given a simulator of the human body and your agent needs to figure out how to move it around.