Summary of Semifactual Explanations For Reinforcement Learning, by Jasmina Gajcin et al.
Semifactual Explanations for Reinforcement Learning
by Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Deep reinforcement learning (DRL) algorithms utilize neural networks to represent agents’ policies, making their decisions challenging to interpret. To advance user trust, increase engagement, and facilitate integration with real-life tasks, it is essential to explain DRL agents’ behavior. This paper proposes a novel approach to generating semifactual explanations for RL systems. Semifactuals provide “even if” scenarios, such as “even if the car were moving twice as slowly, it would still have to swerve to avoid crashing”. While semifactuals are extensively studied in psychology and used in supervised learning, they have not been applied to explain RL decisions. The proposed algorithms, SGRL-Rewind and SGRL-Advance, generate semifactual explanations that are easier to reach, better represent the agent’s policy, and more diverse compared to baselines. This research evaluates the effectiveness of these algorithms in two standard RL environments and conducts a user study to assess participants’ perception of semifactual explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to explain why artificial intelligence (AI) agents make certain decisions. AI agents learn by trying different things, but it’s hard for people to understand why they make certain choices. The researchers want to change that by creating a new way to explain AI decisions using “even if” scenarios. For example, imagine you’re driving and the AI agent says, “Even if I were going 50% slower, I would still have to swerve to avoid an accident.” This helps people understand how different factors affect the outcome and makes it easier for them to work with the AI agent. |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised
