Deep Exploration via Bootstrapped DQN
Machine Learning
2016-07-05 v3 Artificial Intelligence
Systems and Control
Machine Learning
Abstract
Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games.
Keywords
Cite
@article{arxiv.1602.04621,
title = {Deep Exploration via Bootstrapped DQN},
author = {Ian Osband and Charles Blundell and Alexander Pritzel and Benjamin Van Roy},
journal= {arXiv preprint arXiv:1602.04621},
year = {2016}
}