Reinforcement learning (RL) is an area of machine learning where agents learn from interacting with environment to determine actions. RL tools have been widely used in many different engineering systems such as power grid, wireless communications, and autonomous driving etc. A common goal in these decision-making tasks is to determine an optimal policy that minimizes the expected total discounted cost, which is also named risk-neutral approach. Although the risk-neutral approach is quite popular, it does not take the tail of the distributions of the cost into consideration. In the tail of the distribution, the cost may be prohibitively high, even though the probability of happening is low. In safety-critical applications, it is important to consider these rare but consequential events. Given the potential drawbacks of risk-neutral approach in safety critical applications, there is a pressing need to systematically study the risk-sensitive approach, in which one designs decision policies that take the risk into consideration. The goal of this project is to develop a unified framework for the design of efficient algorithms for risk-sensitive RL by systematically employing a class of risk measure named coherent risk measures and develop efficient algorithms that could be implemented in engineering systems. Even though a multitude of risk measures have been extensively studied in the literature and successfully applied to RL, existing work face the following challenges: 1) Mo