Trial without error: Towards safe reinforcement learning via human intervention
During training, model-free reinforcement learning (RL) systems can explore actions that lead to harmful or costly consequences. Having a human “in the loop” and ready to intervene at all times can prevent these mistakes, but is prohibitively expensive for current algorithms. We explore how human ov...
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| Format: | Conference item |
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ACM Digital Library
2018
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