Regularizing Trajectory Optimization with Denoising Autoencoders

Autor:	Boney, Rinu, Di Palo, Norman, Berglund, Mathias, Ilin, Alexander, Kannala, Juho, Rasmus, Antti, Valpola, Harri
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Robotics Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propose to regularize trajectory optimization by means of a denoising autoencoder that is trained on the same trajectories as the model of the environment. We show that the proposed regularization leads to improved planning with both gradient-based and gradient-free optimizers. We also demonstrate that using regularized trajectory optimization leads to rapid initial learning in a set of popular motor control tasks, which suggests that the proposed approach can be a useful tool for improving sample efficiency. Comment: NeurIPS 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.11981 Zobrazit plný text záznamu View this record from Arxiv