<ul><li>Reinforcement Learning (RL) frameworks often face challenges in terms of iteration efficiency and robustness.</li><li>Risk-sensitive policy gradient methods aim to yield more robust policies, but their iteration complexity is not well understood.</li><li>A rigorous analysis of the risk-sensitive policy gradient method reveals an iteration complexity of O(ε^-2) to reach an ε-approximate first-order stationary point.</li><li>Empirical evaluation shows that risk-averse cases can converge and stabilize faster compared to risk-neutral counterparts.</li></ul>

Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis

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