mlsanityData to 7 Oct 2026

Lifelong Learning Metrics

arXiv 2201.08278 ↗First posted 20 Jan 2022cs.AI cs.LGcited by 13

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Abstract (arXiv)

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way. Performers on this program developed systems capable of performing a diverse range of functions, including autonomous driving, real-time strategy, and drone simulation. These systems featured a diverse range of characteristics (e.g., task structure, lifetime duration), and an immediate challenge faced by the program's testing and evaluation team was measuring system performance across these different settings. This document, developed in close collaboration with DARPA and the program performers, outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

19 of 28 references.

References · 19

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2016Progressive Neural Networkscited by 1,332
  3. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  4. 2019Habitat: A Platform for Embodied AI Researchcited by 1,163
  5. 2018Experience Replay for Continual Learningcited by 1,074
  6. 2019Three scenarios for continual learningcited by 610
  7. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  8. 2017CORe50: a New Dataset and Benchmark for Continuous Object Recognitioncited by 287
  9. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 268
  10. 2018Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselinescited by 228
  11. 2018Towards Robust Evaluations of Continual Learningcited by 148
  12. 2021Avalanche: an End-to-End Library for Continual Learningcited by 124
7 more
  1. 2020Continual Deep Learning by Functional Regularisation of Memorable Pastcited by 104
  2. 2019Uncertainty-guided Continual Learning with Bayesian Neural Networkscited by 100
  3. 2021Continual Learning of a Mixed Sequence of Similar and Dissimilar Taskscited by 80
  4. 2020La-MAML: Look-ahead Meta Learning for Continual Learningcited by 51
  5. 2020Meta-Consolidation for Continual Learningcited by 40
  6. 2021CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agentscited by 36
  7. 2020Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgettingcited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

13 papers.

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