  Learning a diverse set of skills by interacting with an environment without
any external supervision is an important challenge. In particular, obtaining a
goal-conditioned agent that can reach any given state is useful in many
applications. We propose a novel method for training such a goal-conditioned
agent without any external rewards or any domain knowledge. We use random walk
to train a reachability network that predicts the similarity between two
states. This reachability network is then used in building goal memory
containing past observations that are diverse and well-balanced. Finally, we
train a goal-conditioned policy network with goals sampled from the goal memory
and reward it by the reachability network and the goal memory. All the
components are kept updated throughout training as the agent discovers and
learns new goals. We apply our method to a continuous control navigation and
robotic manipulation tasks.
