  Designing tax policies that are effective in curbing tax evasion and maximize
state revenues requires a rigorous understanding of taxpayer behavior. This
work explores the problem of determining the strategy a self-interested,
risk-averse tax entity is expected to follow, as it "navigates" - in the
context of a Markov Decision Process - a government-controlled tax environment
that includes random audits, penalties and occasional tax amnesties. Although
simplified versions of this problem have been previously explored, the mere
assumption of risk-aversion (as opposed to risk-neutrality) raises the
complexity of finding the optimal policy well beyond the reach of analytical
techniques. Here, we obtain approximate solutions via a combination of
Q-learning and recent advances in Deep Reinforcement Learning. By doing so, we
i) determine the tax evasion behavior expected of the taxpayer entity, ii)
calculate the degree of risk aversion of the "average" entity given empirical
estimates of tax evasion, and iii) evaluate sample tax policies, in terms of
expected revenues. Our model can be useful as a testbed for "in-vitro" testing
of tax policies, while our results lead to various policy recommendations.
