Summary of Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays, by Qingyuan Wu et al.
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
by Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 introduces Auxiliary-Delayed Reinforcement Learning (AD-RL), a novel method that addresses the challenges of reinforcement learning (RL) in environments with delays between events and their sensory perceptions. AD-RL leverages auxiliary tasks involving short delays to accelerate RL with long delays, without compromising performance in stochastic environments. The method learns a value function for short delays and adjusts it for long delays using bootstrapping and policy improvement techniques. Theoretical analysis shows that this approach can significantly reduce sample complexity. Experimental results on deterministic and stochastic benchmarks demonstrate that AD-RL outperforms state-of-the-art (SOTA) methods in both sample efficiency and policy performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning called reinforcement learning. It’s like teaching a computer to make good decisions without seeing the consequences right away. The authors created a new way to do this, called AD-RL, which helps the computer learn faster and better. They tested it on different scenarios and showed that it works much better than other ways of doing things. This is important because it can help computers make smart choices in all sorts of situations. |
Keywords
* Artificial intelligence * Bootstrapping * Machine learning * Reinforcement learning
