Deep Exploration via Bootstrapped DQN

@inproceedings{Osband2016DeepEV,
  title={Deep Exploration via Bootstrapped DQN},
  author={Ian Osband and Charles Blundell and Alexander Pritzel and Benjamin Van Roy},
  booktitle={NIPS},
  year={2016}
}
Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as -greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms… CONTINUE READING
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