Summary of Hierarchical Universal Value Function Approximators, by Rushiv Arora
Hierarchical Universal Value Function Approximators
by Rushiv Arora
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 This paper presents advancements in building universal approximators for multi-goal collections of reinforcement learning value functions. The authors extend this concept to hierarchical reinforcement learning, introducing hierarchical universal value function approximators (H-UVFAs). This allows for leveraging the benefits of scaling, planning, and generalization expected in temporal abstraction settings. The paper develops supervised and reinforcement learning methods for learning embeddings of states, goals, options, and actions in two hierarchical value functions: Q(s, g, o; θ) and Q(s, g, o, a; θ). The results demonstrate the generalization of HUVFAs, showing they outperform corresponding UVFAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving computers that can make good decisions in complex situations. It’s like having a super smart AI that can solve many problems at once. The authors developed a new way to teach these computers using “options” and “goals”. This helps them learn faster and make better decisions. They tested their idea and found it works really well, even when the situation is very different from what they’ve seen before. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Supervised
