Konsep Inti
The authors propose an innovative approach to unattended containerized reinforcement learning using Webots, emphasizing the separation of simulation and model development environments. They aim to streamline the training process for data scientists without requiring knowledge of simulation software.
Abstrak
An architecture for unattended containerized reinforcement learning with Webots is presented by Tobias Haubold and Petra Linke from the University of Applied Sciences Zwickau, Germany. The paper addresses challenges in reinforcement learning setups where data scientists need familiarity with simulation software. The proposed approach aims to eliminate this requirement by utilizing standalone simulation software, Webots, along with ROS and container technology.
Over recent years, advancements in reinforcement learning have been significant, introducing new algorithms like the dqn agent and tools such as the gym library. The integration of container technology has matured in infrastructure fields, providing a standardized unit for packaging applications and dependencies.
The content delves into the development, deployment, and lifecycle of data science applications within industries. It highlights challenges faced by reinforcement learning setups that necessitate data scientists' knowledge of simulation software. Various approaches are discussed, including MuJoCo-based environments and Unity ml-agents.
Webots emerges as a key component in the proposed architecture due to its open-source nature and support for various robots. The integration with ROS allows seamless communication between real and virtual robots. Deepbots is also mentioned as a framework combining Webots with Open AI gym for reinforcement learning environments.
The paper outlines a detailed example application involving Robotino in a logistical setting within Webots. It emphasizes creating meaningful gymnasium environments for training agents effectively. Different reinforcement learning algorithms are evaluated using tf-agents like dqn agent and reinforce agent.
Current limitations are acknowledged regarding multiple Webots instances per training setup but workarounds are suggested to address these issues. The authors emphasize the practicality of their approach in fully automated reinforcement learning setups that can run unattended.
Statistik
Over 200 hours spent on total training duration across various experiments.
More than 100 training sessions conducted during the research.
Kutipan
"The proposed approach uses standalone simulation software Webots to separate simulation from model development."
"Our research task involves Robotino in logistical settings but aims for applicability to other robots."