Summary of Atradiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories, by Qianlan Yang et al.
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories
by Qianlan Yang, Yu-Xiong Wang
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Adaptive Trajectory Diffuser (ATraDiff) model leverages offline data to learn a generative diffusion model, generating synthetic trajectories that enhance the performance of online reinforcement learning methods. By utilizing ATraDiff, agents can adapt to varying trajectory lengths and mitigate distribution shifts between online and offline data, allowing for improved generalization to new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous agents are trained using sparse rewards in online reinforcement learning (RL), which is a challenging problem due to low data efficiency. Previous work extracted useful knowledge from offline data by learning action distributions and utilizing them to facilitate online RL. However, this approach has limitations since the offline data are fixed, making it difficult to generalize to new tasks. The proposed ATraDiff model generates synthetic trajectories as a form of data augmentation, enhancing online RL performance while adapting to varying trajectory lengths and mitigating distribution shifts. |
Keywords
* Artificial intelligence * Data augmentation * Diffusion model * Generalization * Reinforcement learning
