  Reinforcement Learning (RL) has shown exceptional performance across various
applications, enabling autonomous agents to learn optimal policies through
interaction with their environments. However, traditional RL frameworks often
face challenges in terms of iteration complexity and robustness. Risk-sensitive
RL, which balances expected return and risk, has been explored for its
potential to yield probabilistically robust policies, yet its iteration
complexity analysis remains underexplored. In this study, we conduct a thorough
iteration complexity analysis for the risk-sensitive policy gradient method,
focusing on the REINFORCE algorithm and employing the exponential utility
function. We obtain an iteration complexity of $\mathcal{O}(\epsilon^{-2})$ to
reach an $\epsilon$-approximate first-order stationary point (FOSP). We
investigate whether risk-sensitive algorithms can achieve better iteration
complexity compared to their risk-neutral counterparts. Our theoretical
analysis demonstrates that risk-sensitive REINFORCE can have a reduced number
of iterations required for convergence. This leads to improved iteration
complexity, as employing the exponential utility does not entail additional
computation per iteration. We characterize the conditions under which
risk-sensitive algorithms can achieve better iteration complexity. Our
simulation results also validate that risk-averse cases can converge and
stabilize more quickly after approximately half of the episodes compared to
their risk-neutral counterparts.
