Policy Gradient Methods for Distortion Risk Measures

Understand

We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework.

• Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively.
• We derive a variant of the policy gradient theorem that caters to the DRM objective, and integrate it with a likelihood ratio-based gradient estimation scheme.
• We derive non-asymptotic bounds that establish the convergence of our proposed algorithms to an approximate stationary point of the DRM objective.