“Deep Exploration via Bootstrapped DQN”, Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy2016-02-15 (RL exploration; backlinks; similar)⁠:

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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