Summary of Concept-based Interpretable Reinforcement Learning with Limited to No Human Labels, by Zhuorui Ye and Stephanie Milani and Geoffrey J. Gordon and Fei Fang
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels
by Zhuorui Ye, Stephanie Milani, Geoffrey J. Gordon, Fei Fang
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel training scheme for reinforcement learning (RL) algorithms that integrates interpretable concept-based policies into neural networks. The existing RL methods, which rely on neural network-based policies, often lack interpretability, making it challenging for stakeholders to comprehend and trust the decision-making process. To overcome this limitation, the authors introduce LICORICE, a training scheme that enables efficient learning of a concept-based policy by querying humans only for a small set of data or even without human labels. The scheme involves three main contributions: interleaving concept learning and RL training, using concept ensembles to actively select informative data points for labeling, and decorrelating the concept data with a simple strategy. The authors demonstrate how LICORICE reduces manual labeling efforts to 500 or fewer concept labels in three environments. Additionally, they explore the potential of powerful vision-language models to infer concepts from raw visual inputs without explicit labels at minimal cost to performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem with reinforcement learning (RL). Right now, RL algorithms use neural networks that are hard for people to understand, which makes it difficult for stakeholders to trust the decisions being made. The authors come up with a new way of training these algorithms that uses understandable concepts and doesn’t need as much human help. This helps reduce the amount of work needed to label data, making it more practical. They also show how powerful computer models can be used to understand visual information without needing extra labels. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning
