Summary of Action Gaps and Advantages in Continuous-time Distributional Reinforcement Learning, by Harley Wiltzer et al.
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
by Harley Wiltzer, Marc G. Bellemare, David Meger, Patrick Shafto, Yash Jhaveri
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 A new study investigates the performance of distributional reinforcement learning (DRL) agents when decisions are made at high frequency. The research finds that DRL agents are sensitive to decision frequency and that their action-conditioned return distributions collapse to their underlying policy’s return distribution as frequency increases. The study also defines a new concept called superiority, which is a probabilistic generalization of the advantage, and introduces a superiority-based DRL algorithm. Through simulations in an option-trading domain, the algorithm demonstrates improved controller performance at high decision frequencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well distributional reinforcement learning (DRL) works when decisions are made quickly. They found that DRL gets worse as decisions get more frequent. The study also introduces a new idea called superiority and shows how it can be used to make better algorithms. By testing the algorithm in a pretend trading scenario, they showed that it does work better than before. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
