  Reinforcement learning has shown promise in learning policies that can solve
complex problems. However, manually specifying a good reward function can be
difficult, especially for intricate tasks. Inverse reinforcement learning
offers a useful paradigm to learn the underlying reward function directly from
expert demonstrations. Yet in reality, the corpus of demonstrations may contain
trajectories arising from a diverse set of underlying reward functions rather
than a single one. Thus, in inverse reinforcement learning, it is useful to
consider such a decomposition. The options framework in reinforcement learning
is specifically designed to decompose policies in a similar light. We therefore
extend the options framework and propose a method to simultaneously recover
reward options in addition to policy options. We leverage adversarial methods
to learn joint reward-policy options using only observed expert states. We show
that this approach works well in both simple and complex continuous control
tasks and shows significant performance increases in one-shot transfer
learning.
