Summary of Trust the Model Where It Trusts Itself — Model-based Actor-critic with Uncertainty-aware Rollout Adaption, by Bernd Frauenknecht et al.
Trust the Model Where It Trusts Itself – Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
by Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: Dyna-style model-based reinforcement learning (MBRL) combines model-free agents with predictive transition models through model-based rollouts, raising the critical question “When to trust your model?” Janner et al. (2019) address this by gradually increasing rollout lengths throughout training, but uniform model accuracy is a fallacy that collapses when extrapolating. Instead, we propose asking “Where to trust your model?” using inherent model uncertainty for local accuracy, introducing the Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption (MACURA) algorithm. We demonstrate substantial improvements in data efficiency and performance compared to state-of-the-art deep MBRL methods on the MuJoCo benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about using machines to make decisions by learning from experience. It’s trying to figure out when to trust what it thinks will happen next, which is important for making good choices. The researchers found that just increasing how far ahead it looks doesn’t work because the predictions get less accurate over time. Instead, they came up with a new way to decide when to rely on its predictions and how much to adapt based on uncertainty. This new approach worked really well and was better than what other people have done in the same area. |
Keywords
» Artificial intelligence » Reinforcement learning
