3 papers
cs.AI2026
ClawTrace: Cost-Aware Tracing for LLM Agent Skill Distillation
Boqin Yuan, Yue Su, Renchu Song +2
Skill-distillation pipelines learn reusable rules from LLM agent trajectories, but they lack a key signal: how much each step costs. Without per-step cost, a pipeline cannot distin…
cs.AI2026
LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning
Chang Yao, Jinghui Qin, Kebing Jin +1
Despite achieving remarkable success in complex tasks, Deep Reinforcement Learning (DRL) is still suffering from critical issues in practical applications, such as low data efficie…
cs.LG2024
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Daniel Bogdoll, Jing Qin, Moritz Nekolla +3
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. C…