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Poster
Learning Options via Compression
Yiding Jiang · Evan Liu · Benjamin Eysenbach · J. Zico Kolter · Chelsea Finn

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #609
in Poster Session 6 »

Identifying statistical regularities in solutions to some tasks in multi-task reinforcement learning can accelerate the learning of new tasks.Skill learning offers one way of identifying these regularities by decomposing pre-collected experiences into a sequence of skills.A popular approach to skill learning is maximizing the likelihood of the pre-collected experience with latent variable models,where the latent variables represent the skills. However, there are often many solutions that maximize the likelihood equally well, including degenerate solutions. To address this underspecification, we propose a new objective that combines the maximum likelihood objective with a penalty on the description length of the skills. This penalty incentivizes the skills to maximally extract common structures from the experiences. Empirically, our objective learns skills that solve downstream tasks in fewer samples compared to skills learned from only maximizing likelihood. Further, while most prior works in the offline multi-task setting focus on tasks with low-dimensional observations, our objective can scale to challenging tasks with high-dimensional image observations.

Keywords: hierarchical reinforcement learning · skill learning
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Yiding Jiang (Carnegie Mellon University)
Evan Liu (Stanford University)
Benjamin Eysenbach (CMU)
Benjamin Eysenbach

Assistant professor at Princeton working on self-supervised reinforcement learning (scaling, algorithms, theory, and applications).

J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Chelsea Finn (Stanford)

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