Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …

Recent research on pedestrian trajectory prediction based on deep learning has made
significant progress. However, the previous methods do not deeply explore the relationship …

Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit
constraints followed by expert agents from their demonstration data. As an emerging …

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward
functions and ensuring the safety of the learned policy. To address these challenges, we …

In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for
constrained Markov decision process (CMDP) problems. In standard IRL problems, the …

Learning from Demonstration (LfD) allows robots to mimic human actions. However, these
methods do not model constraints crucial to ensure safety of the learned skill. Moreover …

By incorporating known constraints into the inverse reinforcement learning (IRL) framework,
constrained inverse reinforcement learning (CIRL) can learn behaviors from expert …

Reinforcement learning (RL) has demonstrated impressive performance in decision-making
tasks like embodied control, autonomous driving and financial trading. In many decision …