How can we design agents that pursue a given objective when all feedback mechanisms are influenceable by the agent? Standard RL algorithms assume a secure reward function, and can thus perform poorly in settings where agents can tamper with the reward-generating mechanism. We present a principled solution to the problem of learning from influenceable feedback, which combines approval with a decoupled feedback collection procedure. For a natural class of corruption functions, decoupled approval algorithms have aligned incentives both at convergence and for their local updates. Empirically, they also scale to complex 3D environments where tampering is possible.
@article{arxiv.2011.08827,
title = {Avoiding Tampering Incentives in Deep RL via Decoupled Approval},
author = {Jonathan Uesato and Ramana Kumar and Victoria Krakovna and Tom Everitt and Richard Ngo and Shane Legg},
journal= {arXiv preprint arXiv:2011.08827},
year = {2020}
}