Keywords: quality diversity, reinforcement learning, user-defined, population
TL;DR: We formulate the Quality-Similar Diversity (QSD) problem and propose an efficient population-based RL algorithm to optimize the user-defined diversity at multiple quality levels throughout training.
Abstract: Diversity is a growing research topic in Reinforcement Learning (RL). Previous research on diversity has mainly focused on promoting diversity to encourage exploration and thereby improve quality (the cumulative reward), maximizing diversity subject to quality constraints, or jointly maximizing quality and diversity, known as the quality-diversity problem. In this work, we present the quality-similar diversity problem that features diversity among policies of similar qualities. In contrast to task-agnostic diversity, we focus on task-specific diversity defined by a set of user-specified Behavior Descriptors (BDs). A BD is a scalar function of a trajectory (e.g., the fire action rate for an Atari game), which delivers the type of diversity the user prefers. To derive the gradient of the user-specified diversity with respect to a policy, which is not trivially available, we introduce a set of BD estimators and connect it with the classical policy gradient theorem. Based on the diversity gradient, we develop a population-based RL algorithm to adaptively and efficiently optimize the population diversity at multiple quality levels throughout training. Extensive results on MuJoCo and Atari demonstrate that our algorithm significantly outperforms previous methods in terms of generating user-specified diverse policies across different quality levels.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)