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Deep learning is increasingly used to learn policies for planning problems, yet policies represented by neural networks are difficult to interpret, verify and trust. Existing formal approaches to post-hoc explanations provide concise reasons for a single decision made by an ML model. However, understanding planning policies require explaining sequences of decisions. In this paper, we formulate the problem of finding explanations for the sequence of decisions recommended by a learnt policy in a given state. We show that, under certain assumptions, a minimal explanation for a sequence can be computed by solving a number of single decision explanation problems which is linear in the length of the sequence. We present experimental results of our implementation of this approach for ASNet policies for classical planning domains.