Summary of An Information Theoretic Approach to Interaction-grounded Learning, by Xiaoyan Hu et al.
An Information Theoretic Approach to Interaction-Grounded Learning
by Xiaoyan Hu, Farzan Farnia, Ho-fung Leung
First submitted to arxiv on: 10 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 Variational Information-based Interaction-Grounded Learning (VI-IGL) method tackles Reinforcement Learning (RL) problems where the learner infers an unobserved reward from feedback variables. This approach is particularly useful in settings like Interaction-Grounded Learning, where the goal is to optimize returns by inferring latent binary rewards from interactions with the environment. By enforcing conditional independence assumptions using information-theoretic methods, VI-IGL learns a reward decoder based on conditional mutual information (MI) between context-actions and feedback variables. This framework also leverages variational representations of MI for continuous random variables in RL problems, resulting in a min-max optimization problem. The generalized f-VI-IGL framework is then extended to accommodate various information measures. Numerical results demonstrate improved performance compared to existing IGL-based RL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to help machines learn from interactions with their environment. They call it Variational Information-based Interaction-Grounded Learning (VI-IGL). This method is useful when we don’t know what reward the machine should get for its actions. Instead, it figures out the best rewards by looking at how well it does in different situations. The approach uses special math called information theory to make sure the machine learns the right things. It also helps with learning from continuous random variables, which are important in many real-world applications. Overall, this method shows promise for improving reinforcement learning algorithms. |
Keywords
* Artificial intelligence * Decoder * Optimization * Reinforcement learning
