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

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

Roboschool

To experiment with the methods in this chapter, we will use Roboschool, which uses PyBullet as a physics engine and has 13 environments of various complexity. PyBullet has similar environments, but at the time of writing, it isn't possible to create several instances of the same environment due to an internal OpenGL issue.

In this chapter, we will explore two problems: RoboschoolHalfCheetah-v1, which models a two-legged creature, and RoboschoolAnt-v1, which has four legs. Their state and action spaces are very similar to the Minitaur environment that we saw in Chapter 17, Continuous Action Space: the state includes characteristics from joints, and the actions are activations of those joints. The goal for both is to move as far as possible, minimizing the energy spent. Figure 19.1 shows screenshots of the two environments.

Figure 19.1: Screenshots of two Roboschool environments: RoboschoolHalfCheetah and RoboschoolAnt

To install Roboschool, you need to follow...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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

Roboschool

Unlock this book and the full library FREE for 7 days

Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

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Hands-On Intelligent Agents with OpenAI Gym

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