Summary of When Does Self-prediction Help? Understanding Auxiliary Tasks in Reinforcement Learning, by Claas Voelcker et al.
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
by Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper investigates how auxiliary tasks like observation reconstruction and latent self-prediction affect representation learning in reinforcement learning. The authors examine how these tasks interact with distractions and observation functions in a Markov Decision Process (MDP). A theoretical framework is developed to analyze the learning dynamics of these tasks, including TD learning, under linear model assumptions. The study shows that latent-self prediction is a useful auxiliary task, while observation reconstruction can provide more useful features when used alone. Empirical analysis reveals that the insights from this framework predict behavior beyond linear models in non-linear neural networks, highlighting its practical benefits for applied problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how extra tasks help or hurt learning in a special kind of machine learning called reinforcement learning. The authors want to know what happens when these extra tasks interact with distractions and how we can understand this process. They create a framework to study this and find that one task, latent-self prediction, helps while another, observation reconstruction, helps more when used alone. This study also shows that the insights from this framework work even when using more complex computer models, making it useful for real-world problems. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Representation learning
