Pessimism About Unknown Unknowns Inspires Conservatism
📖 Description
arXiv:2006.08753v1 [cs.AI] 15 Jun 2020Proceedings of Machine Learning Research vol 125: 1?30, 2020 33rd Annual Conference on Learning Theory Pessimism About Unknown Unknowns Inspires Conservatism Michael K. Cohen MICHAEL -K-COHEN .COM University of Oxford Research School of Engineering Science Future of Humanity Institute Marcus Hutter HUTTER 1.NET Google DeepMind Australian National University Editors: Jacob Abernethy and Shivani Agarwal Abstract If we could de?ne the set of all bad outcomes, we could hard-co de an agent which avoids them; however, in suf?ciently complex environments, this is infe asible. We do not know of any general- purpose approaches in the literature to avoiding novel fail ure modes. Motivated by this, we de?ne an idealized Bayesian reinforcement learner which follows a policy that maximizes the worst-case expected reward over a set of world-models. We call this agen t pessimistic, since it optimizes assuming the worst case. A scalar parameter tunes the agen...
📊 Game Impacts
Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
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