Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning

Abstract

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier environment. We show that incremental learning can produce vastly superior results than standard methods by providing a strong baseline method across ten Dex environments. We finally develop a saliency method for qualitative analysis of reinforcement learning, which shows the impact incremental learning has on network attention.

arXiv commentNIPS 2017 submission, 10 pages, 26 figures

References in corpus (5)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Overcoming catastrophic forgetting in neural networks
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Playing Atari with Deep Reinforcement Learning
• Unifying Count-Based Exploration and Intrinsic Motivation