Summary of The Distributional Reward Critic Framework For Reinforcement Learning Under Perturbed Rewards, by Xi Chen et al.
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
by Xi Chen, Zhihui Zhu, Andrew Perrault
First submitted to arxiv on: 11 Jan 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 The proposed distributional reward critic framework can learn under unknown perturbations, preserving optimal policy or achieving comparable rewards. This framework is compatible with any reinforcement learning algorithm, making it a valuable tool for real-world applications where rewards may be noisy, corrupted, or perturbed. By leveraging this framework, agents can adapt to changing environments and optimize their behavior despite uncertainty in the reward signal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research helps robots and computers learn how to behave in uncertain situations. Imagine you’re teaching a robot to do tasks, but it’s getting mixed signals – some are correct, while others are wrong or misleading. This study shows how to design a system that can still learn and adapt even when the rewards (or feedback) are not entirely trustworthy. |
Keywords
* Artificial intelligence * Reinforcement learning
