Frame dropping – the loss of state and reward information due to communication delays or sensor failures – poses a significant challenge for reinforcement learning (RL) agents deployed in the physical world. Existing solutions such as the Decision Transformer under Random Frame Dropping (DeFog) can mitigate performance degradation, but because DeFog is an offline learning method, it struggles to generalise to novel states not adequately represented in its training dataset.
To address this limitation, researchers Yotsufuji, Daiki, Nishihara, Kenta, Shimizu, Shoma, Uchida, Kento, and Shirakawa, Shinichi have developed OnDeFog, an online approach that integrates the mechanisms of DeFog with the Online Decision Transformer (ODT). OnDeFog learns policies through direct environmental interaction, enabling it to handle frame dropping while retaining the ability to adapt to new situations.
How OnDeFog Works
OnDeFog builds on two prior lines of work:
- Decision Transformer (DT): a model that frames RL as a sequence-modelling problem using a transformer architecture.
- DeFog (Decision Transformer under Random Frame Dropping): an offline extension of DT that adds mechanisms to cope with missing states and rewards caused by frame dropping.
By combining DeFog's specialised mechanisms with ODT's online learning capability, OnDeFog can update its policy continuously even when frames are dropped during deployment.
Performance Results
The researchers conducted a comprehensive experimental evaluation. Key findings include:
- OnDeFog achieves superior performance compared to ODT in environments characterised by high dropping frame rates.
- OnDeFog outperforms DeFog on datasets containing a large amount of low-reward data.
The results indicate that the online nature of OnDeFog helps it generalise better than offline DeFog when the training data is noisy or sparse, while its frame-dropping mechanisms allow it to maintain robustness where ODT fails.
| Method | Environment with High Drop Rate | Dataset with Low-Reward Data |
|---|---|---|
| ODT | Lower performance | Not reported |
| DeFog | Moderate performance | Lower performance |
| OnDeFog | Superior performance | Superior performance |
Table: Relative performance of OnDeFog versus ODT and DeFog as reported in the study.
Implications for Real-World AI
While the research is presented in a machine-learning context, its relevance extends to any domain where RL agents operate under imperfect sensing – including robotics in logistics, autonomous vehicles, and industrial automation. For enterprise technology buyers evaluating AI deployment in supply chain or warehouse automation, frame robustness is a critical factor. Systems that can gracefully handle sensor dropouts without retraining from scratch offer higher uptime and lower maintenance costs.
The work also highlights a broader trend: moving from offline to online learning to close the gap between training and deployment conditions. OnDeFog's architecture suggests that hybrid approaches – combining the structural advantages of transformers with online adaptation – may become a standard design pattern for production RL systems.
No information was provided in the source about specific industries, deployment costs, or integration timelines. The paper is available on arXiv under a Creative Commons license.
