Title: CLIC: Curriculum Learning and Imitation for feature Control in non-rewarding environments,
Abstract: In this paper, we propose an unsupervised reinforcement learning agent called
CLIC for Curriculum Learning and Imitation for Control. This agent learns to
control features in its environment without external rewards, and observes the
actions of a third party agent, Bob, who does not necessarily provide explicit
guidance. CLIC selects which feature to train on and what to imitate from Bob's
behavior by maximizing its learning progress. We show that CLIC can effectively
identify helpful behaviors in Bob's actions, and imitate them to control the
environment faster. CLIC can also follow Bob when he acts as a mentor and
provides ordered demonstrations. Finally, when Bob controls features than the
agent cannot, or in presence of a hierarchy between aspects of the environment,
we show that CLIC ignores non-reproducible and already mastered behaviors,
resulting in a greater benefit from imitation.