Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

arXiv:2203.16464v3 Announce Type: replace
Abstract: Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. In addition to desirable evaluation metrics, a high level of interpretability is often required for these models to be reliably utilized. Therefore, explanations that offer insight into the process by which a model maps its inputs onto its outputs are much sought-after. Unfortunately, the current black box nature of …

abstract artificial artificial intelligence arxiv computer computer vision cs.ai cs.lg deep learning evaluation evaluation metrics fields intelligence interpretability language language processing metrics natural natural language natural language processing performances processing reinforcement reinforcement learning tasks through type via vision