arXiv:2502.00684v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) has successfully addressed many complex control problems. However, the neural networks representing policies or values remain opaque, undermining trust in high-stakes applications. While concept-based methods have shown promise in deciphering internal representations in computer vision, applying them to DRL is impeded by the absence of pre-defined semantic concepts in continuous state spaces. In this work, we propose a novel concept-based explanation framework designed to provide fine-grained, neuron-level in
The increasing complexity and adoption of DRL in critical applications necessitates improved methods for understanding and verifying AI decision-making.
Enhanced interpretability for DRL systems will foster trust, accelerate deployment in high-stakes environments, and improve development cycles by making models more debuggable.
The ability to decipher neuron-level reasoning in DRL moves from opaque black-box models towards more transparent, auditable, and reliable AI systems.
- · AI developers
- · High-stakes application sectors (e.g., autonomous vehicles, defense)
- · AI assurance and auditing firms
- · Opaque DRL deployment in critical systems
- · Developers reliant solely on black-box optimization
More widespread and trusted deployment of DRL in sensitive applications.
Increased pressure for regulatory standards around AI interpretability and explainability.
The development of 'interpretable by design' AI architectures becoming a dominant paradigm.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG