Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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Index

Policy experiments

The first model that I trained was with the Height objective and without zeroing the yaw component. A video of the robot executing the policy is available here: https://www.youtube.com/watch?v=u5rDogVYs9E. The movements are not very natural. In particular, the front-right leg is not moving at all. This model is available in the source tree as Chapter18/hw/libhw/t1.py.

As this might be related to the yaw observation component, which is different during the training and inference, the model was retrained with the --zero-yaw command-line option. The result is a bit better: all legs are now moving, but the robot's actions are still not very stable. The video is here: https://www.youtube.com/watch?v=1JVVnWNRi9k. The model used is in Chapter18/hw/libhw/t1zyh.py.

The third experiment was done with a different training objective, HeightOrient, which not only takes into account the height of the model, but also checks that the body of the robot is parallel to the...