Deep Exploration via Bootstrapped DQN

Ian Osband; Charles Blundell; Alexander Pritzel; Benjamin Van Roy

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arXiv · 1602.04621

Deep Exploration via Bootstrapped DQN

Ian Osband·Charles Blundell·Alexander Pritzel·Benjamin Van Roy

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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.

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Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy. 2016-02-15. Deep Exploration via Bootstrapped DQN. https://arxiv.org/abs/1602.04621

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