  Reinforcement learning methods carry a well known bias-variance trade-off in
n-step algorithms for optimal control. Unfortunately, this has rarely been
addressed in current research. This trade-off principle holds independent of
the choice of the algorithm, such as n-step SARSA, n-step Expected SARSA or
n-step Tree backup. A small n results in a large bias, while a large n leads to
large variance. The literature offers no straightforward recipe for the best
choice of this value. While currently all n-step algorithms use a fixed value
of n over the state space we extend the framework of n-step updates by allowing
each state to have its specific n.
  We propose a solution to this problem within the context of human aided
reinforcement learning. Our approach is based on the observation that a human
can learn more efficiently if she receives input regarding the criticality of a
given state and thus the amount of attention she needs to invest into the
learning in that state. This observation is related to the idea that each state
of the MDP has a certain measure of criticality which indicates how much the
choice of the action in that state influences the return. In our algorithm the
RL agent utilizes the criticality measure, a function provided by a human
trainer, in order to locally choose the best stepnumber n for the update of the
Q function.
