2 citations · 2 across the 2 of their papers we have counts for
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cs.AI2017
Universal Reinforcement Learning Algorithms: Survey and Experiments
John Aslanides, Jan Leike, Marcus Hutter
Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal…
cs.AI2017
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
Sean Lamont, John Aslanides, Jan Leike +1
In recent years, work has been done to develop the theory of General Reinforcement Learning (GRL). However, there are few examples demonstrating these results in a concrete way. In…
cs.AI2015★ 2 cited
Sequential Extensions of Causal and Evidential Decision Theory
Tom Everitt, Jan Leike, Marcus Hutter
Moving beyond the dualistic view in AI where agent and environment are separated incurs new challenges for decision making, as calculation of expected utility is no longer straight…