| Autor: |
Nuin, Yue Leire Erro, Lopez, Nestor Gonzalez, Moral, Elias Barba, Juan, Lander Usategui San, Rueda, Alejandro Solano, Vilches, Víctor Mayoral, Kojcev, Risto |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics to train a robot directly from joint states, using traditional robotic tools. We use an state-of-the-art implementation of the Proximal Policy Optimization, Trust Region Policy Optimization and Actor-Critic Kronecker-Factored Trust Region algorithms to learn policies in four different Modular Articulated Robotic Arm (MARA) environments. We support this process using a framework that communicates with typical tools used in robotics, such as Gazebo and Robot Operating System 2 (ROS 2). We evaluate several algorithms in modular robots with an empirical study in simulation. |
| Databáze: |
arXiv |
| Externí odkaz: |
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