Guiding Skill Discovery with Foundation Models

Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat, Edward S. Hu

Keywords: reinforcement learning, unsupervised skill discovery, foundation models

TL;DR: We use foundation models to guide unsupervised skill discovery methods to learn diverse and desirable behaviours in RL.

Abstract: Learning diverse skills without hand-crafted reward functions could potentially accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on maximizing the diversity of skills without considering human preferences, which leads to undesirable behaviors and possibly dangerous skills. For instance, a cheetah robot trained using previous methods learns to roll in all directions to maximize skill diversity, whereas we would prefer it to run without flipping or entering hazardous areas. In this work, we propose a **Fo**undation model **G**uided (FoG) skill discovery method, which incorporates human intentions into skill discovery through foundation models. Specifically, FoG extracts a score function from foundation models to evaluate states based on human intentions, assigning higher values to desirable states and lower to undesirable ones. These scores are then used to re-weight the rewards of skill discovery algorithms. By optimizing the re-weighted skill discovery rewards, FoG successfully learns to eliminate undesirable behaviors, such as flipping or rolling, and to avoid hazardous areas in both state-based and pixel-based tasks. Interestingly, we show that FoG can discover skills involving behaviors that are difficult to define. Interactive visualisations are available from: https://sites.google.com/view/iclr-fog

Primary Area: reinforcement learning

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Submission Number: 2449