Web: http://arxiv.org/abs/2110.05415

June 24, 2022, 1:11 a.m. | Yousef Emam, Gennaro Notomista, Paul Glotfelter, Zsolt Kira, Magnus Egerstedt

cs.LG updates on arXiv.org arxiv.org

Reinforcement Learning (RL) has been shown to be effective in many scenarios.
However, it typically requires the exploration of a sufficiently large number
of state-action pairs, some of which may be unsafe. Consequently, its
application to safety-critical systems remains a challenge. An increasingly
common approach to address safety involves the addition of a safety layer that
projects the RL actions onto a safe set of actions. In turn, a difficulty for
such frameworks is how to effectively couple RL with …

arxiv learning reinforcement reinforcement learning

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