* 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

Reward (Mis)design for autonomous driving

Artificial Intelligence, 2023
W. Bradley Knox*, Alessandro Allievi*, Holger Banzhaf*, Felix Schmitt*, Peter Stone

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaw…

Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning

AAAI, 2023
Bo Liu*, Yihao Feng*, Qiang Liu*, Peter Stone

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics tasks, sample efficiency is of the utmost importance for GCRL since, by default, the …

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

AAAI, 2023
Serena Booth*, W. Bradley Knox*, Julie Shah*, Scott Niekum*, Peter Stone, Alessandro Allievi*

In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance metric is often sparse. For example, a true task metric might encode a reward of 1 upon success and 0 otherwise. These sparse task metrics can be hard to learn from, so in pr…

  • 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.