Summary of Generalized Gaussian Temporal Difference Error For Uncertainty-aware Reinforcement Learning, by Seyeon Kim et al.
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by Seyeon Kim, Joonhun Lee, Namhoon Cho, Sungjun Han, Wooseop Hwang
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Probability (math.PR); 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 presents a novel framework for generalized Gaussian error modeling in deep reinforcement learning to improve uncertainty estimation and mitigation. Conventional methods often assume a zero-mean Gaussian distribution for TD errors, leading to inaccurate representations and compromised uncertainty estimation. The proposed approach incorporates higher-order moments, particularly kurtosis, to enhance flexibility and accuracy of error distribution modeling. The authors examine the influence of the shape parameter on aleatoric uncertainty and provide a closed-form expression demonstrating an inverse relationship. Additionally, they propose a theoretically grounded weighting scheme to address epistemic uncertainty by leveraging the generalized Gaussian distribution. Experimental results with policy gradient algorithms demonstrate significant performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to learn in situations where there’s not enough information. Right now, computers make mistakes because they don’t understand how uncertain they are. The researchers created a new method that lets them model uncertainty more accurately by considering different shapes of the uncertainty distribution. This helps them make better decisions and reduces mistakes. They tested their approach with computer simulations and found it improved performance. |
Keywords
» Artificial intelligence » Reinforcement learning
