Deep Exploration via Bootstrapped DQN
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.
NIPS