Risk-Sensitive Reinforcement Learning via Policy Gradient Search. (arXiv:1810.09126v3 [cs.LG] UPDATED)

The objective in a traditional reinforcement learning (RL) problem is to find
a policy that optimizes the expected value of a performance metric such as the
infinite-horizon cumulative discounted or long-run average cost/reward. In
practice, optimizing the expected value alone may not be satisfactory, in that
it may be desirable to incorporate the notion of risk into the optimization
problem formulation, either in the objective or as a constraint. Various risk
measures have been proposed in the literature, e.g., exponential …

arxiv gradient learning policy reinforcement reinforcement learning risk search