Safe Exploration Techniques for Reinforcement Learning - An Overview

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223262" target="_blank" >RIV/68407700:21230/14:00223262 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/~peckama2/papers/safe_exploration_overview_lncs.pdf" target="_blank" >http://cmp.felk.cvut.cz/~peckama2/papers/safe_exploration_overview_lncs.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-13823-7" target="_blank" >10.1007/978-3-319-13823-7</a>

Result language
angličtina
Original language name
Safe Exploration Techniques for Reinforcement Learning - An Overview
Original language description
We overview different approaches to safety in (semi)autonomous robotics. Part icularly, we focus on how to achieve safe behavior of a robot if it is requested to perform ex ploration of unknown states. Presented methods are studied from the viewpoint ofreinforcement learning, a partially-supervised machine learning method. To collect training data for this a lgorithm, the robot is required to freely explore the state space - which can lead to possibly dangerous situations. The role of safe explorationis to provide a framework allowing explora tion while preserving safety. The examined methods range from simple algorithms to sophisticat ed methods based on previous experience or state prediction. Our overview also addresses the i ssues of how to define safety in the real-world applications (apparently absolute safety is un achievable in the continuous and random real world). In the conclusion we also suggest several ways that are worth researching more thoroughly.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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