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"Evolving Neural Programs for Continuous Learning", Dennis Wilson, colloque de l'IRIT

on the June 15, 2017


Dennis Wilson, PhD student in the research group VORTEX, will talk about "Evolving Neural Programs for Continuous Learning".

Abstract: Artificial neural networks (ANNs) have recently made large advances in the field of continuous control tasks. Both on-policy and off-policy reinforcement learning (RL) algorithms which train ANNs have shown impressive results in tasks such as classic RL control tasks, robotic control, and video game playing with pixel input. However, these training methods are limited by their inability to generalize to different tasks after learning a specific task, termed catastro phic forgetting, and their need for a large set of training examples. These key features of continuous learning, the ability to learn new skills while retaining previous knowledge and the ability to learn on a small set of examples, are found in biologic neural networks and contemporary neuroscience has greatly advanced understanding of some of their underlying mechanisms.

In this talk, I will examine existing artificial neural models, ranging from deep learning to evolutionary and developmental methods, as they relate to continuous learning. I will then discuss an evolutionary model, currently under development, which explores existing neural models and discovers new models for competition in an open continuous learning environment that assesses the catastrophic forgetting and learning rate of each model.
Updated on June 7, 2017