A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
[article]
2017
arXiv
pre-print
In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to interrupt an agent in order to prevent dangerous situations from happening. Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them. The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past
arXiv:1704.02882v2
fatcat:sep4udawhvf2jiaom3wymrgqie