mlsanityData to 7 Oct 2026

Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework

arXiv 2306.01839 ↗First posted 2 Jun 2023cs.RO cs.LGcited by 6

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Abstract (arXiv)

In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a transferring approach with a parameter-compositional formulation. We investigate ways to improve the training of multi-task reinforcement learning which serves as the foundation for transferring. Then we conduct a number of transferring experiments on various manipulation tasks. Experimental results demonstrate that the proposed approach can have improved performance in the multi-task training stage, and further show effective transferring in terms of both sample efficiency and performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

28 of 37 references.

References · 28

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  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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  5. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
  6. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
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  9. 2019Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLcited by 359
  10. 2016Successor Features for Transfer in Reinforcement Learningcited by 331
  11. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 256
  12. 2017Distral: Robust Multitask Reinforcement Learningcited by 231
16 more
  1. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 226
  2. 2016Modular Multitask Reinforcement Learning with Policy Sketchescited by 200
  3. 2015Actor-Mimic: Deep Multitask and Transfer Reinforcement Learningcited by 176
  4. 2021Multi-Task Reinforcement Learning with Context-based Representationscited by 169
  5. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 138
  6. 2016Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfercited by 124
  7. 2017Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learningcited by 97
  8. 2021panda-gym: Open-source goal-conditioned environments for robotic learningcited by 56
  9. 2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
  10. 2017Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuningcited by 48
  11. 2022PaCo: Parameter-Compositional Multi-Task Reinforcement Learningcited by 40
  12. 2020Universal Successor Features for Transfer Reinforcement Learningcited by 20
  13. 20216-DoF Contrastive Grasp Proposal Networkcited by 12
  14. 2022Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learningcited by 5
  15. 2021A Simple Approach to Continual Learning by Transferring Skill Parameterscited by 2
  16. 2023A Simple Approach for General Task-Oriented Picking using Placing constraintscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.

6 papers.

  1. 2023Interactive Planning Using Large Language Models for Partially Observable Robotics Taskscited by 27
  2. 2025Skill Expansion and Composition in Parameter Spacecited by 13
  3. 2025One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planningcited by 3
  4. 2024Automating Transfer of Robot Task Plans using Functorial Data Migrationscited by 0
  5. 2026MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answeringcited by 0
  6. 2026HyLoVQA: Dynamic Hypernetwork-Generated Low-Rank Adaptation for Continual Visual Question Answeringcited by 0