Summary of Decision Theory-guided Deep Reinforcement Learning For Fast Learning, by Zelin Wan et al.
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
by Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. Singh
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL) approach addresses the cold start problem in DRL by integrating decision theory principles. This enhances agents’ initial performance and robustness in complex environments, leading to more efficient and reliable convergence during learning. The paper investigates DT-guided DRL in two problem contexts: cart pole and maze navigation challenges. Experimental results show that DT-guided DRL facilitates effective initial guidance for DRL agents and promotes a structured exploration strategy, especially in large state spaces. Compared to regular DRL, DT-guided DRL achieves significantly higher rewards, with an 184% increase during the initial training phase and up to 53% more reward at convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision Theory-guided Deep Reinforcement Learning is a new way to help robots learn how to solve problems on their own. Right now, these robots can get stuck when they first start learning because they don’t know what to do. This paper shows that by using decision theory, which is a set of rules about making good choices, we can make the robot’s initial performance better and more reliable. The results are impressive: the new approach gets up to 184% more rewards than the old way when it first starts learning, and even after it reaches its peak, it still does better. This could be an important step in helping robots work better with humans. |
Keywords
* Artificial intelligence * Reinforcement learning
