  Markov Decision Processes (MDPs) are an effective way to formally describe
many Machine Learning problems. In fact, recently MDPs have also emerged as a
powerful framework to model financial trading tasks. For example, financial
MDPs can model different market scenarios. However, the learning of a
(near-)optimal policy for each of these financial MDPs can be a very
time-consuming process, especially when nothing is known about the policy to
begin with. An alternative approach is to find a similar financial MDP for
which we have already learned its policy, and then reuse such policy in the
learning of a new policy for a new financial MDP. Such a knowledge transfer
between market scenarios raises several issues. On the one hand, how to measure
the similarity between financial MDPs. On the other hand, how to use this
similarity measurement to effectively transfer the knowledge between financial
MDPs. This paper addresses both of these issues. Regarding the first one, this
paper analyzes the use of three similarity metrics based on conceptual,
structural and performance aspects of the financial MDPs. Regarding the second
one, this paper uses Probabilistic Policy Reuse to balance the
exploitation/exploration in the learning of a new financial MDP according to
the similarity of the previous financial MDPs whose knowledge is reused.
