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

Similarity metrics for Different Market Scenarios in Abides