Training a Four Legged Robot via Deep Reinforcement Learning and Multibody Simulation

Autor:	Alessandro Tasora, Simone Benatti, Dario Mangoni
Rok vydání:	2019
Předmět:	Artificial neural network Simple (abstract algebra) business.industry Computer science Control (management) Reinforcement learning Robot Multibody simulation Artificial intelligence Tuple Legged robot business
Zdroj:	Multibody Dynamics 2019 ISBN: 9783030231316
DOI:	10.1007/978-3-030-23132-3_47
Popis:	In this paper we use the Proximal Policy Optimization (PPO) deep reinforcement learning algorithm to train a Neural Network to control a four-legged robot in simulation. Reinforcement learning in general can learn complex behavior policies from simple state-reward tuples datasets and PPO in particular has proved its effectiveness in solving complex tasks with continuous states and actions. Moreover, since it is model-free, it is general and can adapt to changes in the environment or in the robot itself.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b4917569ce0d90dbcba72e395c728372 https://doi.org/10.1007/978-3-030-23132-3_47 Zobrazit plný text záznamu