  Learning a policy capable of moving an agent between any two states in the
environment is important for many robotics problems involving navigation and
manipulation. Due to the sparsity of rewards in such tasks, applying
reinforcement learning in these scenarios can be challenging. Common approaches
for tackling this problem include reward engineering with auxiliary rewards,
requiring domain-specific knowledge or changing the objective.
  In this work, we introduce a method based on region-growing that allows
learning in an environment with any pair of initial and goal states. Our
algorithm first learns how to move between nearby states and then increases the
difficulty of the start-goal transitions as the agent's performance improves.
This approach creates an efficient curriculum for learning the objective
behavior of reaching any goal from any initial state. In addition, we describe
a method to adaptively adjust expansion of the growing region that allows
automatic adjustment of the key exploration hyperparameter to environments with
different requirements. We evaluate our approach on a set of simulated
navigation and manipulation tasks, where we demonstrate that our algorithm can
efficiently learn a policy in the presence of sparse rewards.
