Diversity-Enriched Option-Critic

Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales.

The option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting.

However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a shrinking set of task relevant options.

These occurrences not only void the need for temporal abstraction, they also affect performance.

In this paper, we tackle these problems by learning a diverse set of options.

We introduce an information-theoretic intrinsic reward, which augments the task reward, as well as a novel termination objective, in order to encourage behavioral diversity in the option set.

We show empirically that our proposed method is capable of learning options end-to-end on several discrete and continuous control tasks, outperforms option-critic by a wide margin.

Furthermore, we show that our approach sustainably generates robust, reusable, reliable and interpretable options, in contrast to option-critic.