Summary of Uncertainty Representations in State-space Layers For Deep Reinforcement Learning Under Partial Observability, by Carlos E. Luis et al.
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
First submitted to arxiv on: 25 Sep 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 The proposed standalone Kalman filter layer is designed to incorporate uncertainty into the hidden state representation of reinforcement learning architectures. This innovative approach uses closed-form Gaussian inference in linear state-space models and trains it end-to-end within a model-free architecture to maximize returns. The Kalman filter layer processes sequential data using a parallel scan, which scales logarithmically with the sequence length, making it a drop-in replacement for other recurrent layers. By explicitly modeling uncertainty, this approach excels in tasks where reasoning about hidden state is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make decisions when you don’t have all the information. Right now, most computers use old models that can’t handle uncertainty well. The authors created a new type of layer called the Kalman filter layer that can handle uncertainty and train it with a computer program. This new layer is like a superpower for computers, allowing them to make better decisions when they don’t know everything. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
