  Reinforcement learning (RL) algorithms are typically limited to learning a
single solution of a specified task, even though there often exists diverse
solutions to a given task. Compared with learning a single solution, learning a
set of diverse solutions is beneficial because diverse solutions enable robust
few-shot adaptation and allow the user to select a preferred solution. Although
previous studies have showed that diverse behaviors can be modeled with a
policy conditioned on latent variables, an approach for modeling an infinite
set of diverse solutions with continuous latent variables has not been
investigated. In this study, we propose an RL method that can learn infinitely
many solutions by training a policy conditioned on a continuous or discrete
low-dimensional latent variable. Through continuous control tasks, we
demonstrate that our method can learn diverse solutions in a data-efficient
manner and that the solutions can be used for few-shot adaptation to solve
unseen tasks.
