  The use of Reinforcement Learning (RL) agents in practical applications
requires the consideration of suboptimal outcomes, depending on the familiarity
of the agent with its environment. This is especially important in
safety-critical environments, where errors can lead to high costs or damage. In
distributional RL, the risk-sensitivity can be controlled via different
distortion measures of the estimated return distribution. However, these
distortion functions require an estimate of the risk level, which is difficult
to obtain and depends on the current state. In this work, we demonstrate the
suboptimality of a static risk level estimation and propose a method to
dynamically select risk levels at each environment step. Our method ARA
(Automatic Risk Adaptation) estimates the appropriate risk level in both known
and unknown environments using a Random Network Distillation error. We show
reduced failure rates by up to a factor of 7 and improved generalization
performance by up to 14% compared to both risk-aware and risk-agnostic agents
in several locomotion environments.
