Summary of Skill or Luck? Return Decomposition Via Advantage Functions, by Hsiao-ru Pan et al.
Skill or Luck? Return Decomposition via Advantage Functions
by Hsiao-Ru Pan, Bernhard Schölkopf
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a new method for reinforcement learning called Off-policy Direct Advantage Estimation (Off-policy DAE), which allows agents to learn from off-policy data without relying on importance sampling or truncating actions. This is achieved by decomposing the return of a trajectory into parts caused by the agent’s actions (skill) and parts outside of its control (luck). The method can speed up learning and provide better policy optimization performance when ignoring off-policy corrections leads to suboptimal results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machines learn from experiences they didn’t directly cause, which is important for efficient training. It shows how a technique called Direct Advantage Estimation (DAE) can be used with data that wasn’t collected on purpose, without needing special tricks or reducing the impact of unrelated actions. This makes it easier to create better agents that learn quickly. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
