  It is of significance for an agent to learn a widely applicable and
general-purpose policy that can achieve diverse goals including images and text
descriptions. Considering such perceptually-specific goals, the frontier of
deep reinforcement learning research is to learn a goal-conditioned policy
without hand-crafted rewards. To learn this kind of policy, recent works
usually take as the reward the non-parametric distance to a given goal in an
explicit embedding space. From a different viewpoint, we propose a novel
unsupervised learning approach named goal-conditioned policy with intrinsic
motivation (GPIM), which jointly learns both an abstract-level policy and a
goal-conditioned policy. The abstract-level policy is conditioned on a latent
variable to optimize a discriminator and discovers diverse states that are
further rendered into perceptually-specific goals for the goal-conditioned
policy. The learned discriminator serves as an intrinsic reward function for
the goal-conditioned policy to imitate the trajectory induced by the
abstract-level policy. Experiments on various robotic tasks demonstrate the
effectiveness and efficiency of our proposed GPIM method which substantially
outperforms prior techniques.
