  In risk-averse reinforcement learning (RL), the goal is to optimize some risk
measure of the returns. A risk measure often focuses on the worst returns out
of the agent's experience. As a result, standard methods for risk-averse RL
often ignore high-return strategies. We prove that under certain conditions
this inevitably leads to a local-optimum barrier, and propose a soft risk
mechanism to bypass it. We also devise a novel Cross Entropy module for risk
sampling, which (1) preserves risk aversion despite the soft risk; (2)
independently improves sample efficiency. By separating the risk aversion of
the sampler and the optimizer, we can sample episodes with poor conditions, yet
optimize with respect to successful strategies. We combine these two concepts
in CeSoR - Cross-entropy Soft-Risk optimization algorithm - which can be
applied on top of any risk-averse policy gradient (PG) method. We demonstrate
improved risk aversion in maze navigation, autonomous driving, and resource
allocation benchmarks, including in scenarios where standard risk-averse PG
completely fails.
