mlsanityData to 7 Oct 2026

Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training

arXiv 2405.13445 ↗First posted 22 May 2024cs.LG cs.AIcited by 1

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become increasingly prevalent. However, the inherent variability in state variables and action spaces among personalized agents poses significant aggregation challenges for traditional federated learning algorithms. To tackle these challenges, we introduce the Federated Split Decision Transformer (FSDT), an innovative framework designed explicitly for AI agent decision tasks. The FSDT framework excels at navigating the intricacies of personalized agents by harnessing distributed data for training while preserving data privacy. It employs a two-stage training process, with local embedding and prediction models on client agents and a global transformer decoder model on the server. Our comprehensive evaluation using the benchmark D4RL dataset highlights the superior performance of our algorithm in federated split learning for personalized agents, coupled with significant reductions in communication and computational overhead compared to traditional centralized training approaches. The FSDT framework demonstrates strong potential for enabling efficient and privacy-preserving collaborative learning in applications such as autonomous driving decision systems. Our findings underscore the efficacy of the FSDT framework in effectively leveraging distributed offline reinforcement learning data to enable powerful multi-type agent decision systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 27 of its 33 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

27 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun
  2. 2019Behavior Regularized Offline Reinforcement Learning26 Nov

Other references · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  6. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  7. 2019Towards Federated Learning at Scale: System Designcited by 826
  8. 2019Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learningcited by 750
  9. 2017Federated Multi-Task Learningcited by 669
  10. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  11. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  12. 2022Multi-Game Decision Transformerscited by 212
13 more
  1. 2022Online Decision Transformercited by 194
  2. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  3. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 74
  4. 2023Hyper-Decision Transformer for Efficient Online Policy Adaptationcited by 32
  5. 2019How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learningcited by 29
  6. 2022FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classificationcited by 26
  7. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  8. 2023FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformercited by 23
  9. 2023Large Sequence Models for Sequential Decision-Making: A Surveycited by 19
  10. 2022Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutationcited by 10
  11. 2022FedFormer: Contextual Federation with Attention in Reinforcement Learningcited by 8
  12. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  13. 2024P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformercited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocationcited by 3

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