Summary of Echoes Of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning, by Alex Christopher Stutts et al.
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
by Alex Christopher Stutts, Danilo Erricolo, Theja Tulabandhula, Amit Ranjan Trivedi
First submitted to arxiv on: 11 Feb 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 This paper introduces Calibrated Evidential Quantile Regression in Deep Q Networks (CEQR-DQN), a novel statistical approach that incorporates uncertainty awareness in model-free distributional reinforcement learning. The proposed algorithm combines deep evidential learning with quantile calibration based on conformal inference, enabling explicit, sample-free computations of global uncertainty. This addresses limitations of traditional methods in computational and statistical efficiency, as well as handling out-of-distribution observations. CEQR-DQN is tested on miniaturized Atari games (MinAtar) and shows improved scores and learning speed compared to similar existing frameworks. The ability to rigorously evaluate uncertainty improves exploration strategies and can serve as a blueprint for other algorithms requiring uncertainty awareness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making computers learn better in situations where they’re not sure what’s happening. They introduce a new way of doing this, called CEQR-DQN, which is good at estimating how certain it is about its decisions. This is important because computers need to be able to make decisions when things don’t go as planned. The new method works well on a set of simplified video games and can help computers learn faster and better in uncertain situations. |
Keywords
* Artificial intelligence * Inference * Regression * Reinforcement learning
