Summary of Offline Trajectory Generalization For Offline Reinforcement Learning, by Ziqi Zhao et al.
Offline Trajectory Generalization for Offline Reinforcement Learning
by Ziqi Zhao, Zhaochun Ren, Liu Yang, Fajie Yuan, Pengjie Ren, Zhumin Chen, jun Ma, Xin Xin
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Offline reinforcement learning aims to learn policies from static datasets of previously collected trajectories, but existing methods either constrain the learned policy or utilize model-based virtual environments. These methods suffer from poor generalization and trivial improvement from low-quality simulation. To address these issues, this paper proposes offline trajectory generalization through world transformers for offline reinforcement learning (OTTO). OTTO uses casual Transformers to predict state dynamics and immediate reward, then generates high-rewarded trajectory simulation by perturbing the offline data. The algorithm can be integrated with existing offline RL methods to enhance their capabilities. Conducting extensive experiments on D4RL benchmark datasets verifies that OTTO outperforms state-of-the-art offline RL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is a way to learn policies from past experiences without needing to interact with the environment anymore. The current methods for this have some limitations, like not being able to generalize well or improving only slightly. A new approach called OTTO (offline trajectory generalization through world transformers) tries to solve these problems by using special transformers that can predict how states will change and what rewards you’ll get. This helps generate new and better experiences to learn from. The authors of this paper show that OTTO works well on certain datasets and outperforms other methods. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
