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One of the main challenges in reinforcement learning (RL) is that the agenthas to make decisions that would influence the future performance withouthaving complete knowledge of the environment. Dynamically adjusting the levelof epistemic risk during the learning process can help to achieve reliablepolicies in safety-critical settings with better efficiency. In this work, wepropose a new framework, Distributional RL with Online Risk Adaptation(DRL-ORA). This framework quantifies both epistemic and implicit aleatoryuncertainties in a unified manner and dynamically adjusts the epistemic risklevels by solving a total variation minimization problem online. The selectionof risk levels is performed efficiently via a grid search using aFollow-The-Leader-type algorithm, where the offline oracle corresponds to a"satisficing measure" under a specially modified loss function. We show thatDRL-ORA outperforms existing methods that rely on fixed risk levels or manuallydesigned risk level adaptation in multiple classes of tasks.

DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaption