Planning and Learning with Stochastic Action Sets
  In many practical uses of reinforcement learning (RL) the set of actions
available at a given state is a random variable, with realizations governed by
an exogenous stochastic process. Somewhat surprisingly, the foundations for
such sequential decision processes have been unaddressed. In this work, we
formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to
provide these foundations. We show that optimal policies and value functions in
this model have a structure that admits a compact representation. From an RL
perspective, we show that Q-learning with sampled action sets is sound. In
model-based settings, we consider two important special cases: when individual
actions are available with independent probabilities; and a sampling-based
model for unknown distributions. We develop poly-time value and policy
iteration methods for both cases; and in the first, we offer a poly-time linear
programming solution.
