  It remains challenging to deploy existing risk-averse approaches to
real-world applications. The reasons are multi-fold, including the lack of
global optimality guarantee and the necessity of learning from long-term
consecutive trajectories. Long-term consecutive trajectories are prone to
involving visiting hazardous states, which is a major concern in the
risk-averse setting. This paper proposes Short-Term VOlatility-controlled
Policy Search (STOPS), a novel algorithm that solves risk-averse problems by
learning from short-term trajectories instead of long-term trajectories.
Short-term trajectories are more flexible to generate, and can avoid the danger
of hazardous state visitations. By using an actor-critic scheme with an
overparameterized two-layer neural network, our algorithm finds a globally
optimal policy at a sublinear rate with proximal policy optimization and
natural policy gradient, with effectiveness comparable to the state-of-the-art
convergence rate of risk-neutral policy-search methods. The algorithm is
evaluated on challenging Mujoco robot simulation tasks under the mean-variance
evaluation metric. Both theoretical analysis and experimental results
demonstrate a state-of-the-art level of STOPS' performance among existing
risk-averse policy search methods.
