Distributional Reinforcement Learning with Online Risk-awareness Adaption
The author introduces a novel framework, DRL-ORA, for Distributional Reinforcement Learning that dynamically adjusts risk levels online to handle uncertainties. By solving a total variation minimization problem, the framework quantifies and adapts to epistemic uncertainties efficiently.
