  Having a perfect model to compute the optimal policy is often infeasible in
reinforcement learning. It is important in high-stakes domains to quantify and
manage risk induced by model uncertainties. Entropic risk measure is an
exponential utility-based convex risk measure that satisfies many reasonable
properties. In this paper, we propose an entropic risk constrained policy
gradient and actor-critic algorithms that are risk-averse to the model
uncertainty. We demonstrate the usefulness of our algorithms on several problem
domains.
