第34届IEEE/ACM自动化软件工程国际会议(ASE 2019)将于2019年11月11日至15日在圣地亚哥举行。该会议是自动化软件工程的首要研究论坛。每年,它汇集了学术界和工业界的研究人员和实践者,讨论自动化、分析、设计、实现、测试和维护大型软件系统的基础、技术和工具。 官网链接:https://2019.ase-conferences.org/

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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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