  In reinforcement learning, temporal abstraction in the action space,
exemplified by action repetition, is a technique to facilitate policy learning
through extended actions. However, a primary limitation in previous studies of
action repetition is its potential to degrade performance, particularly when
sub-optimal actions are repeated. This issue often negates the advantages of
action repetition. To address this, we propose a novel algorithm named
Uncertainty-aware Temporal Extension (UTE). UTE employs ensemble methods to
accurately measure uncertainty during action extension. This feature allows
policies to strategically choose between emphasizing exploration or adopting an
uncertainty-averse approach, tailored to their specific needs. We demonstrate
the effectiveness of UTE through experiments in Gridworld and Atari 2600
environments. Our findings show that UTE outperforms existing action repetition
algorithms, effectively mitigating their inherent limitations and significantly
enhancing policy learning efficiency.
