This work concerns with discrete-time Markov decision processes on a denumerable state space. Assuming that the decision maker is risk-averse with constant risk-sensitivity coefficient, the performance of a control policy is measured by an average criterion associated with a non-negative and bounded cost function. Under conditions ensuring that the optimal average cost is constant, but not necessarily determined via the average cost optimality equation, it is shown that a discounted criterion can be used to approximate the optimal average index.

中文翻译：

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风险规避下交流平均马尔可夫决策链的折扣方法

这项工作涉及可枚举状态空间上的离散时间马尔可夫决策过程。假设决策者是具有恒定风险敏感系数的风险规避者，则控制策略的性能由与非负和有界成本函数相关联的平均标准来衡量。在保证最优平均成本恒定但不一定通过平均成本最优方程确定的条件下，表明可以使用贴现标准来近似最优平均指数。