Diversity is All You Need: Learning Skills without a Reward Function

Intelligent creatures can explore their environments and learn useful skills without
supervision. In this paper, we propose “Diversity is All You Need”(DIAYN), a
method for learning useful skills without a reward function. Our proposed method
learns skills by maximizing an information theoretic objective using a maximum
entropy policy. On a variety of simulated robotic tasks, we show that this simple
objective results in the unsupervised emergence of diverse skills, such as walking
and jumping. In a number of reinforcement learning benchmark environments, our
method is able to learn a skill that solves the benchmark task despite never receiving
the true task reward. We show how pretrained skills can provide a good parameter
initialization for downstream tasks, and can be composed hierarchically to solve
complex, sparse reward tasks. Our results suggest that unsupervised discovery of
skills can serve as an effective pretraining mechanism for overcoming challenges
of exploration and data efficiency in reinforcement learning.

• Algorithms and Theory

• Machine Intelligence