3 papers
cs.LG2026
Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping
Simon Sinong Zhan, Philip Wang, Qingyuan Wu +4
In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and…
cs.CR2025
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems
Ruochen Jiao, Shaoyuan Xie, Justin Yue +5
Large Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their i…
cs.LG2024
Inverse Delayed Reinforcement Learning
Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan +6
Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding feat…