Summary of A Multi-step Loss Function For Robust Learning Of the Dynamics in Model-based Reinforcement Learning, by Abdelhakim Benechehab et al.
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
by Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper proposes a novel approach to model-based reinforcement learning by introducing a multi-step objective to train one-step models. The traditional method relies on simulating trajectories from one-step models, but this can lead to compounding errors as the trajectory length increases. To address this issue, the authors suggest using a weighted sum of mean squared error (MSE) loss at various future horizons. This new loss function is particularly effective when dealing with noisy data, which is common in real-world environments. The paper demonstrates the effectiveness of this approach by studying its properties in two simple cases and applying it to various tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in model-based reinforcement learning. When we use one-step models to predict what might happen next, our predictions get worse as we look further ahead because small mistakes add up. The authors came up with a new way to train these models that works better when the data is noisy (which it often is). They show this new method does much better than the old one in different tasks and datasets. |
Keywords
* Artificial intelligence * Loss function * Mse * Reinforcement learning
