Summary of Tractable and Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation, by Taehyun Cho et al.
Tractable and Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
by Taehyun Cho, Seungyub Han, Kyungjae Lee, Seokhun Ju, Dohyeong Kim, Jungwoo Lee
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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 proposed paper investigates distributional reinforcement learning’s effectiveness in capturing environmental stochasticity. It presents a regret analysis for general value function approximation in a finite episodic Markov decision process setting. The authors introduce the concept of Bellman unbiasedness, which enables exact learning via statistical functional dynamic programming. They also show that approximating the infinite-dimensional return distribution with moment functionals is crucial for unbiased learning. To achieve this, they develop the provably efficient algorithm SF-LSVI, which obtains a regret bound of O(d_E H^3/2√K), where H is the horizon, K is the number episodes, and d_E is the eluder dimension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machines can learn from experience. It’s about a special way to make machines better at making decisions by capturing patterns in what happens around them. The researchers come up with new ideas for understanding this method better and create an efficient algorithm to help it work faster and more accurately. This is important because it could lead to machines that are even better at doing tasks we care about. |
Keywords
» Artificial intelligence » Reinforcement learning
