Summary of Tilted Quantile Gradient Updates For Quantile-constrained Reinforcement Learning, by Chenglin Li et al.
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
by Chenglin Li, Guangchun Ruan, Hua Geng
First submitted to arxiv on: 17 Dec 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 paper proposes a new paradigm for safe reinforcement learning (RL), which learns reward-maximizing policies with guarantees of safety. The traditional expectation-based approach to expressing safety constraints is shown to be ineffective, and instead, the authors use quantile-constrained RL, which provides higher levels of safety without approximation. The method directly estimates quantile gradients through sampling and proves theoretical convergence. A tilted update strategy for quantile gradients compensates for asymmetric distributional density, leading to better return performance. Experiments demonstrate that the proposed model meets safety requirements while outperforming state-of-the-art benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way of learning policies in reinforcement learning that makes sure they are safe. Traditional methods don’t work well because they assume things will happen in certain ways, but this can lead to unsafe actions. The authors instead use a method called quantile-constrained RL, which directly measures how likely something is to happen and adjusts the policy accordingly. This approach allows for safer policies that also perform better than current state-of-the-art methods. |
Keywords
» Artificial intelligence » Reinforcement learning
