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Randomized Prior Functions for Deep Reinforcement Learning
Ian Osband · John Aslanides · Albin Cassirer

Wed Dec 05 01:15 PM -- 01:20 PM (PST) @ Room 220 CD

Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.

Author Information

Ian Osband (Google Deepmind)
John Aslanides (DeepMind)
Albin Cassirer (DeepMind)

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