  In recent years, the integration of Automated Planning (AP) and Reinforcement
Learning (RL) has seen a surge of interest. To perform this integration, a
general framework for Sequential Decision Making (SDM) would prove immensely
useful, as it would help us understand how AP and RL fit together. In this
preliminary work, we attempt to provide such a framework, suitable for any
method ranging from Classical Planning to Deep RL, by drawing on concepts from
Probability Theory and Bayesian inference. We formulate an SDM task as a set of
training and test Markov Decision Processes (MDPs), to account for
generalization. We provide a general algorithm for SDM which we hypothesize
every SDM method is based on. According to it, every SDM algorithm can be seen
as a procedure that iteratively improves its solution estimate by leveraging
the task knowledge available. Finally, we derive a set of formulas and
algorithms for calculating interesting properties of SDM tasks and methods,
which make possible their empirical evaluation and comparison.
