Subwords as Skills: Tokenization for Sparse-Reward Reinforcement
  Learning
David Yunis Justin Jung Falcon Dai Matthew Walter
Exploration in sparse-reward reinforcement learning is difficult due to the
requirement of long, coordinated sequences of actions in order to achieve any
reward. Moreover, in continuous action spaces there are an infinite number of
possible actions, which only increases the difficulty of exploration. One class
of methods designed to address these issues forms temporally extended actions,
often called skills, from interaction data collected in the same domain, and
optimizes a policy on top of this new action space. Typically such methods
require a lengthy pretraining phase, especially in continuous action spaces, in
order to form the skills before reinforcement learning can begin. Given prior
evidence that the full range of the continuous action space is not required in
such tasks, we propose a novel approach to skill-generation with two
components. First we discretize the action space through clustering, and second
we leverage a tokenization technique borrowed from natural language processing
to generate temporally extended actions. Such a method outperforms baselines
for skill-generation in several challenging sparse-reward domains, and requires
orders-of-magnitude less computation in skill-generation and online rollouts.