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Asynchronous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks
Author(s)
Date Issued
July 2, 2017
Type
Conference Paper
Abstract
In this paper, we present the method based on asynchronous deep reinforcement learning adapted for the mobile robot navigation with supervised auxiliary tasks. We apply the hybrid Asynchronous Advantage Actor-Critic (A3C) algorithm CPU/GPU based on TensorFlow. The mobile robot is simulated as the navigation tasks on the OpenAI-Gym-Gazebo-based environment with the collaboration with ROS Multimaster. The supervised auxiliary tasks include the depth predictions and the robot position estimation. The simulated mobile robot shows the capability to learn to navigate only the input from raw RGB-image and also perform recognition of the place on the map. We also show that the combination of all possible auxiliary tasks leads to the different learning rate.
Citation
2017 2nd International Conference on Robotics and Automation Engineering Icrae 2017, 2017-December, 68-72, 2017