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Temporally Abstract Partial Models
[article]
2021
arXiv
pre-print
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a
arXiv:2108.03213v1
fatcat:mb7g2ybtpjcvfgf3fzpz2nlcbm