* External authors



A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Megan M. Baker*

Alexander New*

Mario Aguilar-Simon*

Ziad Al-Halah*

Sébastien M. R. Arnold*

Ese Ben-Iwhiwhu*

Andrew P. Brna*

Ethan Brooks*

Ryan C. Brown*

Zachary Daniels*

Anurag Daram*

Fabien Delattre*

Ryan Dellana*

Eric Eaton*

Haotian Fu*

Kristen Grauman*

Jesse Hostetler*

Shariq Iqbal*

Cassandra Kent*

Nicholas Ketz*

Soheil Kolouri*

George Konidaris*

Dhireesha Kudithipudi*

Seungwon Lee*

Michael L. Littman*

Sandeep Madireddy*

Jorge A. Mendez*

Eric Q. Nguyen*

Christine D. Piatko*

Praveen K. Pilly*

Aswin Raghavan*

Abrar Rahman*

Santhosh Kumar Ramakrishnan*

Neale Ratzlaff*

Andrea Soltoggio*

Peter Stone

Indranil Sur*

Zhipeng Tang*

Saket Tiwari*

Kyle Vedder*

Felix Wang*

Zifan Xu*

Angel Yanguas-Gil*

Harel Yedidsion*

Shangqun Yu*

Gautam K. Vallabha*

* External authors



Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of “Lifelong Learning” systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development — both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

Related Publications

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning.

International Conference on Intelligent Robots and Systems, 2023
Xiaohan Zhang*, Yifeng Zhu*, Yan Ding*, Yuqian Jiang*, Yuke Zhu*, Peter Stone, Shiqi Zhang*

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A welldeveloped state space enables the desirable distribution of limited computational resources between task planning an…

Event Tables for Efficient Experience Replay

CoLLAs, 2023
Varun Kompella, Thomas Walsh, Samuel Barrett, Peter R. Wurman, Peter Stone

Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper introduces Stratified Sampling from Event Tables (SSET), whi…

Composing Efficient, Robust Tests for Policy Selection

UAI, 2023
Dustin Morrill, Thomas Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone

Modern reinforcement learning systems produce many high-quality policies throughout the learning process. However, to choose which policy to actually deploy in the real world, they must be tested under an intractable number of environmental conditions. We introduce RPOSST, a…

  • HOME
  • Publications
  • A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.