The depletion of common-pool resources like fisheries is an indicative example of a market failure. Markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriation (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.

翻译：渔业等共有资源枯竭是市场失灵的典型例子。市场不考虑消极的外部效应;一些参与者对其他人造成的间接成本,例如过度征用(减少未来存量,从而给每个人带来丰收)的成本。 事实证明,对市场价格进行适当干预是相当具有挑战性的。 我们提出一种切实可行的方法,通过一个深厚的学习决策者代理人计算市场价格和分配,并在其他学习代理人的环境中运作。我们的决策者允许我们调整价格,以实现各种目标,如可持续性和资源浪费、公平、买方和卖方福利等。 作为我们发现的一个亮点,我们的决策者在稀缺的资源环境中维持资源可持续性,相对于市场平衡结果而言,要成功得多。