mlsanityData to 7 Oct 2026

OnDeFog: Online Decision Transformer under Frame Dropping

arXiv 2606.19721 ↗First posted 18 Jun 2026cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

Abstract (arXiv)

In challenging real-world reinforcement learning applications, communication delays or sensor failures often cause frame dropping, in which the agent cannot receive the dropped states and associated rewards. To address the performance degradation caused by frame dropping, the Decision Transformer under Random Frame Dropping (DeFog) was developed by incorporating additional mechanisms into the decision transformer to tackle frame dropping. Although DeFog can mitigate performance degradation in frame-dropping environments, since DeFog is an offline learning method, it struggles to effectively generalize to novel states not adequately represented in the training dataset. In this study, we propose OnDeFog, which integrates the mechanisms in DeFog with the online decision transformer (ODT), an online reinforcement learning method that learns policies through direct environmental interaction. Comprehensive experimental evaluation demonstrates that our proposed OnDeFog achieves superior performance compared to ODT in environments characterized by high dropping frame rate and outperforms DeFog on datasets containing a large amount of low-reward data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 19 references that are in the map. First posted on arXiv 18 Jun 2026.

Builds on

14 of 19 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Decision Transformer under Random Frame Dropping3 Mar
  2. 2022Online Decision Transformer11 Feb
  3. 2021Decision Transformer: Reinforcement Learning via Sequence Modeling2 Jun

Other references · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  5. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
  6. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  7. 2019Large Batch Optimization for Deep Learning: Training BERT in 76 minutescited by 671
  8. 2019A Survey of Deep Reinforcement Learning in Video Gamescited by 73
  9. 2020Reinforcement Learning with Random Delayscited by 56
  10. 2021Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delayscited by 20
  11. 2022Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestationcited by 4

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  1. 2024Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers
  2. 2026Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer
  3. 2025A Comparison Between Decision Transformers and Traditional Offline Reinforcement Learning Algorithms
  4. 2026Decoupling Return-to-Go for Efficient Decision Transformer
  5. 2026Active Offline-to-Online Reinforcement Learning
  6. 2023Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
  7. 2024In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
  8. 2023Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias