Continual Robot Learning with Constructive Neural Networks

Authors:
Axel Großmann;Riccardo Poli
Affiliations:
-;-
Venue:
EWLR-6 Proceedings of the 6th European Workshop on Learning Robots
Year:
1997

Citing 4
Cited 2

Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching

Machine Learning
Robot Learning

Robot Learning
The Emergence of Default Hierarchies in Learning Classifier Systems

Proceedings of the 3rd International Conference on Genetic Algorithms
Reinforcement learning: a survey

Journal of Artificial Intelligence Research

Teaching a robot to perform tasks with voice commands

MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
AIBO goes to school: basic math

CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an approach for combining reinforcement learning, learning by imitation, and incremental hierarchical development. We apply this approach to a realistic simulated mobile robot that learns to perform a navigation task by imitating the movements of a teacher and then continues to learn by receiving reinforcement. The behaviours of the robot are represented as sensation-action rules in a constructive high-order neural network. Preliminary experiments are reported which show that incremental, hierarchical development, bootstrapped by imitative learning, allows the robot to adapt to changes in its environment during its entire lifetime very efficiently, even if only delayed reinforcements are given.