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Tom Everitt
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Publications
- 2021
- [j1]Tom Everitt, Marcus Hutter, Ramana Kumar, Victoria Krakovna:
Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective. Synth. 198(27): 6435-6467 (2021) - [i19]Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg:
Shaking the foundations: delusions in sequence models for interaction and control. CoRR abs/2110.10819 (2021) - 2019
- [i13]Tom Everitt, Marcus Hutter:
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective. CoRR abs/1908.04734 (2019) - 2018
- [c12]Tom Everitt, Gary Lea, Marcus Hutter:
AGI Safety Literature Review. IJCAI 2018: 5441-5449 - [i12]Tom Everitt, Gary Lea, Marcus Hutter:
AGI Safety Literature Review. CoRR abs/1805.01109 (2018) - 2017
- [c11]Tobias Wängberg, Mikael Böörs, Elliot Catt, Tom Everitt, Marcus Hutter:
A Game-Theoretic Analysis of the Off-Switch Game. AGI 2017: 167-177 - [c10]Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter:
Count-Based Exploration in Feature Space for Reinforcement Learning. IJCAI 2017: 2471-2478 - [i10]Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg:
Reinforcement Learning with a Corrupted Reward Channel. CoRR abs/1705.08417 (2017) - [i9]Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter:
Count-Based Exploration in Feature Space for Reinforcement Learning. CoRR abs/1706.08090 (2017) - [i8]Tobias Wängberg, Mikael Böörs, Elliot Catt, Tom Everitt, Marcus Hutter:
A Game-Theoretic Analysis of the Off-Switch Game. CoRR abs/1708.03871 (2017) - 2016
- [c8]Tom Everitt, Daniel Filan, Mayank Daswani, Marcus Hutter:
Self-Modification of Policy and Utility Function in Rational Agents. AGI 2016: 1-11 - [c7]Tom Everitt, Marcus Hutter:
Avoiding Wireheading with Value Reinforcement Learning. AGI 2016: 12-22 - [c6]Jarryd Martin, Tom Everitt, Marcus Hutter:
Death and Suicide in Universal Artificial Intelligence. AGI 2016: 23-32 - [i6]Tom Everitt, Daniel Filan, Mayank Daswani, Marcus Hutter:
Self-Modification of Policy and Utility Function in Rational Agents. CoRR abs/1605.03142 (2016) - [i5]Tom Everitt, Marcus Hutter:
Avoiding Wireheading with Value Reinforcement Learning. CoRR abs/1605.03143 (2016) - [i4]Jarryd Martin, Tom Everitt, Marcus Hutter:
Death and Suicide in Universal Artificial Intelligence. CoRR abs/1606.00652 (2016) - [i3]Tom Everitt, Tor Lattimore, Marcus Hutter:
Free Lunch for Optimisation under the Universal Distribution. CoRR abs/1608.04544 (2016) - 2015
- [c5]Tom Everitt, Jan Leike, Marcus Hutter:
Sequential Extensions of Causal and Evidential Decision Theory. ADT 2015: 205-221 - [c4]Tom Everitt, Marcus Hutter:
Analytical Results on the BFS vs. DFS Algorithm Selection Problem. Part I: Tree Search. Australasian Conference on Artificial Intelligence 2015: 157-165 - [c3]Tom Everitt, Marcus Hutter:
Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search. Australasian Conference on Artificial Intelligence 2015: 166-178 - [i2]Tom Everitt, Jan Leike, Marcus Hutter:
Sequential Extensions of Causal and Evidential Decision Theory. CoRR abs/1506.07359 (2015) - [i1]Tom Everitt, Marcus Hutter:
A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem. CoRR abs/1509.02709 (2015) - 2014
- [c2]Tom Everitt, Tor Lattimore, Marcus Hutter:
Free Lunch for optimisation under the universal distribution. IEEE Congress on Evolutionary Computation 2014: 167-174 - [c1]Tansu Alpcan, Tom Everitt, Marcus Hutter:
Can we measure the difficulty of an optimization problem? ITW 2014: 356-360
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