mlsanityData to 14 Oct 2026

Parametric-Task MAP-Elites

arXiv 2402.01275 ↗First posted 2 Feb 2024cs.NE cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task algorithms only solve a finite set of tasks, even when the tasks originate from a continuous space. In this paper, we introduce Parametric-Task MAP-Elites (PT-ME), a new black-box algorithm for continuous multi-task optimization problems. This algorithm (1) solves a new task at each iteration, effectively covering the continuous space, and (2) exploits a new variation operator based on local linear regression. The resulting dataset of solutions makes it possible to create a function that maps any task parameter to its optimal solution. We show that PT-ME outperforms all baselines, including the deep reinforcement learning algorithm PPO on two parametric-task toy problems and a robotic problem in simulation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 66 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

12 of 66 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2018An Introduction to Deep Reinforcement Learningcited by 206
  4. 2014Robots that can adapt like animalscited by 204
  5. 2019Covariance Matrix Adaptation for the Rapid Illumination of Behavior Spacecited by 78
  6. 2021Differentiable Quality Diversitycited by 72
  7. 2013Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robotscited by 58
  8. 2022Covariance Matrix Adaptation MAP-Annealingcited by 25
  9. 2020Quality Diversity for Multi-task Optimizationcited by 23
  10. 2021Half a Dozen Real-World Applications of Evolutionary Multitasking, and Morecited by 12
  11. 2020Strategies for Using Proximal Policy Optimization in Mobile Puzzle Gamescited by 6
  12. 2023Multi-Task Multi-Behavior MAP-Elitescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025(θl, θu)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spacescited by 4
  2. 2026Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learningcited by 1
  3. 2026Multi-Task Optimization over Networks of Taskscited by 0
  4. 2026Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildingscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
  2. 2024Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
  3. 2024Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
  4. 2024Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
  5. 2025MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
  6. 2025Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
  7. 2026Task Specialization Fine-Tuning for Contextual Reinforcement Learning
  8. 2024PWM: Policy Learning with Multi-Task World Models