  One typical assumption in inverse reinforcement learning (IRL) is that human
experts act to optimize the expected utility of a stochastic cost with a fixed
distribution. This assumption deviates from actual human behaviors under
ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such
gap by assuming that humans act according to a random cost with respect to a
set of subjectively distorted distributions instead of a fixed one. Such
assumption provides the additional flexibility to model human's risk
preferences, represented by a risk envelope, in safe-critical tasks. However,
like other learning from demonstration techniques, RS-IRL could also suffer
inefficient learning due to redundant demonstrations. Inspired by the concept
of active learning, this research derives a probabilistic disturbance sampling
scheme to enable an RS-IRL agent to query expert support that is likely to
expose unrevealed boundaries of the expert's risk envelope. Experimental
results confirm that our approach accelerates the convergence of RS-IRL
algorithms with lower variance while still guaranteeing unbiased convergence.

Machine Learning;Robotics;Machine Learning