Risk-sensitive Actor-free Policy via Convex Optimization
Ruoqi Zhang Jens Sjölund
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.