arXiv:2607.07235v1 Announce Type: new Abstract: Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging when the action space is continuous. We introduce ORCAID, a novel method for extracting interpretable rule-based policies from RL agents operating in mixed continuous-discrete environments with continuous action spaces. Our main contribution is an efficient oblique decision tree training algorithm that partitions the state space by hyperplanes and fits local linear models.
The increasing complexity and opacity of deep reinforcement learning models are driving urgent demand for greater interpretability, especially in deployment-critical applications.
Improved explainability in RL systems, particularly those with continuous action spaces, accelerates their adoption in real-world scenarios where trust and verification are paramount.
The ability to extract human-understandable rules from complex RL policies fundamentally changes debugging, auditing, and regulatory compliance for AI systems.
- · AI developers
- · Regulators applying AI
- · Industries deploying complex autonomous systems
- · AI ethics research
- · Black-box AI proponents
- · Developers reliant on ad-hoc debugging
Increased trust and adoption of AI in critical domains requiring explainability.
Faster development and deployment cycles for sophisticated autonomous agents due to easier debugging and verification.
New regulatory frameworks and standards emerging to mandate interpretability for AI systems, particularly self-driving vehicles or industrial robotics.
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