arXiv Artificial Intelligence

Explaining Reinforcement Learning Decisions in Self-adaptive Systems

1 hour ago 18.08.2026 07:00 👁 2 views
Explaining Reinforcement Learning Decisions in Self-adaptive Systems

Quick summary

arXiv:2608.14620v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explana

Key takeaways

  • arXiv:2608.14620v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand.
  • This can lead to diminished user trust, and makes for a more challenging verification of systems.
  • To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings.

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗