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Sequential Extensions of Causal and Evidential Decision Theory.

, , and . ADT, volume 9346 of Lecture Notes in Computer Science, page 205-221. Springer, (2015)

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Self-Modification of Policy and Utility Function in Rational Agents., , , and . AGI, volume 9782 of Lecture Notes in Computer Science, page 1-11. Springer, (2016)Reinforcement Learning with a Corrupted Reward Channel., , , , and . CoRR, (2017)AGI Safety Literature Review., , and . IJCAI, page 5441-5449. ijcai.org, (2018)Sequential Extensions of Causal and Evidential Decision Theory., , and . ADT, volume 9346 of Lecture Notes in Computer Science, page 205-221. Springer, (2015)How RL Agents Behave When Their Actions Are Modified., and . AAAI, page 11586-11594. AAAI Press, (2021)Free Lunch for optimisation under the universal distribution., , and . IEEE Congress on Evolutionary Computation, page 167-174. IEEE, (2014)A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem., and . CoRR, (2015)Analytical Results on the BFS vs. DFS Algorithm Selection Problem. Part I: Tree Search., and . Australasian Conference on Artificial Intelligence, volume 9457 of Lecture Notes in Computer Science, page 157-165. Springer, (2015)Avoiding Wireheading with Value Reinforcement Learning., and . AGI, volume 9782 of Lecture Notes in Computer Science, page 12-22. Springer, (2016)Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective., , , and . Synth., 198 (27): 6435-6467 (2021)