  Temporal abstraction is key to scaling up learning and planning in
reinforcement learning. While planning with temporally extended actions is well
understood, creating such abstractions autonomously from data has remained
challenging. We tackle this problem in the framework of options [Sutton, Precup
& Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options
and propose a new option-critic architecture capable of learning both the
internal policies and the termination conditions of options, in tandem with the
policy over options, and without the need to provide any additional rewards or
subgoals. Experimental results in both discrete and continuous environments
showcase the flexibility and efficiency of the framework.
