  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.
