  We present a temporally layered architecture (TLA) for temporally adaptive
control with minimal energy expenditure. The TLA layers a fast and a slow
policy together to achieve temporal abstraction that allows each layer to focus
on a different time scale. Our design draws on the energy-saving mechanism of
the human brain, which executes actions at different timescales depending on
the environment's demands. We demonstrate that beyond energy saving, TLA
provides many additional advantages, including persistent exploration, fewer
required decisions, reduced jerk, and increased action repetition. We evaluate
our method on a suite of continuous control tasks and demonstrate the
significant advantages of TLA over existing methods when measured over multiple
important metrics. We also introduce a multi-objective score to qualitatively
assess continuous control policies and demonstrate a significantly better score
for TLA. Our training algorithm uses minimal communication between the slow and
fast layers to train both policies simultaneously, making it viable for future
applications in distributed control.
