Summary of Which Experiences Are Influential For Rl Agents? Efficiently Estimating the Influence Of Experiences, by Takuya Hiraoka et al.
Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
by Takuya Hiraoka, Guanquan Wang, Takashi Onishi, Yoshimasa Tsuruoka
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In reinforcement learning (RL), experience replay plays a crucial role, as stored experiences affect agent performance. Understanding how these experiences influence performance is vital for identifying problematic experiences in underperforming agents. Leave-one-out (LOO) estimation methods can estimate this influence but are computationally costly. To address this, we propose Policy Iteration with Turn-over Dropout (PIToD), a method that efficiently estimates experience influence. We evaluate PIToD’s accuracy and efficiency compared to LOO and demonstrate its application in improving RL agent performance by identifying and removing negatively influential experiences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re training an artificial intelligence (AI) to make decisions, like a computer playing chess. The AI learns from past experiences, which can either help or hinder its progress. In this paper, scientists developed a new way to understand how these experiences affect the AI’s performance. They created a method called Policy Iteration with Turn-over Dropout (PIToD) that can quickly and accurately identify when an experience is holding back the AI. By removing these negative influences, they were able to significantly improve the AI’s performance. |
Keywords
» Artificial intelligence » Dropout » Reinforcement learning
