  Traditional reinforcement learning methods optimize agents without
considering safety, potentially resulting in unintended consequences. In this
paper, we propose an optimal actor-free policy that optimizes a risk-sensitive
criterion based on the conditional value at risk. The risk-sensitive objective
function is modeled using an input-convex neural network ensuring convexity
with respect to the actions and enabling the identification of globally optimal
actions through simple gradient-following methods. Experimental results
demonstrate the efficacy of our approach in maintaining effective risk control.
