On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning

Pore, A. and Aragon-Camarasa, G. (2020) On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning. In: International Conference on Robotics and Automation (ICRA 2020), Paris, France, 31 May - 04 Jun 2020, ISBN 9781728173955 (doi: 10.1109/ICRA40945.2020.9197262)

Abstract

We present a behaviour-based reinforcement learning approach, inspired by Brook’s subsumption architecture, in which simple fully connected networks are trained as reactive behaviours. Our working assumption is that a pick and place robotic task can be simplified by leveraging domain knowledge of a robotics developer to decompose and train reactive behaviours; namely, approach, grasp, and retract. Then the robot autonomously learns how to combine reactive behaviours via an Actor-Critic architecture. We use an Actor-Critic policy to determine the activation and inhibition mechanisms of the reactive behaviours in a particular temporal sequence. We validate our approach in a simulated robot environment where the task is about picking a block and taking it to a target position while orienting the gripper from a top grasp. The latter represents an extra degree-of-freedom of which current end-to-end reinforcement learning approaches fail to generalise. Our findings suggest that robotic learning can be more effective if each behaviour is learnt in isolation and then combined them to accomplish the task. That is, our approach learns the pick and place task in 8,000 episodes, which represents a drastic reduction in the number of training episodes required by an end-to-end approach ( 95,000 episodes) and existing state-of-the-art algorithms.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Aragon Camarasa, Dr Gerardo
• Authors: Pore, A., and Aragon-Camarasa, G.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2577-087X
• ISBN: 9781728173955
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 IEEE International Conference on Robotics and Automation (ICRA)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Related URLs

• Organisation