  Traditional competitive markets do not account for negative externalities;
indirect costs that some participants impose on others, such as the cost of
over-appropriating a common-pool resource (which diminishes future stock, and
thus harvest, for everyone). Quantifying appropriate interventions to market
prices has proven to be quite challenging. We propose a practical approach to
computing market prices and allocations via a deep reinforcement learning
policymaker agent, operating in an environment of other learning agents. Our
policymaker allows us to tune the prices with regard to diverse objectives such
as sustainability and resource wastefulness, fairness, buyers' and sellers'
welfare, etc. As a highlight of our findings, our policymaker is significantly
more successful in maintaining resource sustainability, compared to the market
equilibrium outcome, in scarce resource environments.
