  The policy gradients of the expected return objective can react slowly to
rare rewards. Yet, in some cases agents may wish to emphasize the low or high
returns regardless of their probability. Borrowing from the economics and
control literature, we review the risk-sensitive value function that arises
from an exponential utility and illustrate its effects on an example. This
risk-sensitive value function is not always applicable to reinforcement
learning problems, so we introduce the particle value function defined by a
particle filter over the distributions of an agent's experience, which bounds
the risk-sensitive one. We illustrate the benefit of the policy gradients of
this objective in Cliffworld.
