  We address the problem of efficient exploration for transition model learning
in the relational model-based reinforcement learning setting without extrinsic
goals or rewards. Inspired by human curiosity, we propose goal-literal babbling
(GLIB), a simple and general method for exploration in such problems. GLIB
samples relational conjunctive goals that can be understood as specific,
targeted effects that the agent would like to achieve in the world, and plans
to achieve these goals using the transition model being learned. We provide
theoretical guarantees showing that exploration with GLIB will converge almost
surely to the ground truth model. Experimentally, we find GLIB to strongly
outperform existing methods in both prediction and planning on a range of
tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic
manipulation task implemented in the PyBullet physics simulator. Video:
https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTB
