SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
英文标题：SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
arXiv:2608.09967v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-base
打开原文相关 2.4 · 机会 1
2.1 KB
612 B
380 B
9.1 KB
32.7 KB
22.8 KB
24.2 KB
34.7 KB
中文
arXiv:2608.09967v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-base
中文全文翻译正在补齐，可先查看下方英文原文。
English / 原文
BibTeX formatted citation
×