Provably Safe PAC-MDP Exploration Using Analogies

Melrose Roderick; Vaishnavh Nagarajan; J. Zico Kolter

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arXiv · 2007.03574

Provably Safe PAC-MDP Exploration Using Analogies

Melrose Roderick·Vaishnavh Nagarajan·J. Zico Kolter

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Abstract

A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safety (needed to avoid catastrophic failure). Although a growing line of work in reinforcement learning has investigated this area of "safe exploration," most existing techniques either 1) do not guarantee safety during the actual exploration process; and/or 2) limit the problem to a priori known and/or deterministic transition dynamics with strong smoothness assumptions. Addressing this gap, we propose Analogous Safe-state Exploration (ASE), an algorithm for provably safe exploration in MDPs with unknown, stochastic dynamics. Our method exploits analogies between state-action pairs to safely learn a near-optimal policy in a PAC-MDP sense. Additionally, ASE also guides exploration towards the most task-relevant states, which empirically results in significant improvements in terms of sample efficiency, when compared to existing methods.

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Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter. 2020-07-07. Provably Safe PAC-MDP Exploration Using Analogies. https://arxiv.org/abs/2007.03574

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