  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 notion of affordances for
options, and develop temporally abstract partial option models, that take into
account the fact that an option might be affordable only in certain situations.
We analyze the trade-offs between estimation and approximation error in
planning and learning when using such models, and identify some interesting
special cases. Additionally, we demonstrate empirically the potential impact of
partial option models on the efficiency of planning.
