Summary of Any-step Dynamics Model Improves Future Predictions For Online and Offline Reinforcement Learning, by Haoxin Lin et al.
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
by Haoxin Lin, Yu-Yan Xu, Yihao Sun, Zhilong Zhang, Yi-Chen Li, Chengxing Jia, Junyin Ye, Jiaji Zhang, Yang Yu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Model-based reinforcement learning methods offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. The challenge lies in accurately predicting sequential steps in the dynamics model, which is hindered by bootstrapping prediction errors that accumulate during model roll-out. To mitigate this issue, we propose the Any-step Dynamics Model (ADM) that reduces bootstrapping prediction to direct prediction, allowing for variable-length plans as inputs for predicting future states without frequent bootstrapping. We design two algorithms, ADMPO-ON and ADMPO-OFF, which apply ADM in online and offline model-based frameworks, respectively. In the online setting, ADMPO-ON demonstrates improved sample efficiency compared to previous state-of-the-art methods. In the offline setting, ADMPO-OFF not only demonstrates superior performance compared to recent state-of-the-art offline approaches but also offers better quantification of model uncertainty using only a single ADM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model-based reinforcement learning helps computers learn new skills by making predictions about what will happen next. But predicting these future steps can be tricky because the computer’s predictions get stuck in a loop, leading to errors. To fix this problem, we created a new way to predict called Any-step Dynamics Model (ADM). This model lets computers use different plans to make predictions without getting stuck. We tested ADM in two ways: one where the computer learns as it goes and another where it uses pre-trained models. In both cases, our model performed better than previous approaches. |
Keywords
* Artificial intelligence * Bootstrapping * Reinforcement learning
