  Temporal abstractions in the form of options have been shown to help
reinforcement learning (RL) agents learn faster. However, despite prior work on
this topic, the problem of discovering options through interaction with an
environment remains a challenge. In this paper, we introduce a novel
meta-gradient approach for discovering useful options in multi-task RL
environments. Our approach is based on a manager-worker decomposition of the RL
agent, in which a manager maximises rewards from the environment by learning a
task-dependent policy over both a set of task-independent discovered-options
and primitive actions. The option-reward and termination functions that define
a subgoal for each option are parameterised as neural networks and trained via
meta-gradients to maximise their usefulness. Empirical analysis on gridworld
and DeepMind Lab tasks show that: (1) our approach can discover meaningful and
diverse temporally-extended options in multi-task RL domains, (2) the
discovered options are frequently used by the agent while learning to solve the
training tasks, and (3) that the discovered options help a randomly initialised
manager learn faster in completely new tasks.
