Summary of Quasimetric Value Functions with Dense Rewards, by Khadichabonu Valieva and Bikramjit Banerjee
Quasimetric Value Functions with Dense Rewards
by Khadichabonu Valieva, Bikramjit Banerjee
First submitted to arxiv on: 13 Sep 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 In this paper, the authors investigate goal-conditioned reinforcement learning (GCRL) and its application to challenging robotics tasks. GCRL generalizes traditional RL by allowing for parametrizable goals. Recent studies have shown that the optimal value function in GCRL has a quasimetric structure, which enables targeted neural architectures. However, these analyses were limited to sparse reward settings. The authors demonstrate that the key property of quasimetrics, the triangle inequality, is preserved under dense rewards as well. They identify the condition necessary for this preservation and show that it can only improve sample complexity. This opens up opportunities for training efficient neural architectures with dense rewards. The authors evaluate their proposal in 12 standard benchmark environments featuring challenging continuous control tasks and find that training a quasimetric value function in their dense reward setting outperforms sparse rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a type of learning called goal-conditioned reinforcement learning (GCRL). It’s useful for robots to learn how to do things. GCRL is like traditional reinforcement learning, but it lets the robot know what goals it should be working towards. The authors found that if they used dense rewards instead of sparse rewards, the robot would get better at learning. This means that in the future, we might be able to use robots to help us with tasks that are hard for humans to do. |
Keywords
* Artificial intelligence * Reinforcement learning
