6 papers
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…
Directly Forecasting Belief for Reinforcement Learning with Delays
Qingyuan Wu, Yuhui Wang, Simon Sinong Zhan +6
Reinforcement learning (RL) with delays is challenging as sensory perceptions lag behind the actual events: the RL agent needs to estimate the real state of its environment based o…
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…
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…
Variational Delayed Policy Optimization
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +5
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning…
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +6
Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either s…