While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

The number of states in a dynamic process is exponential in the number of objects, making
reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to …

In applications of offline reinforcement learning to observational data, such as in healthcare
or education, a general concern is that observed actions might be affected by unobserved …

In offline imitation learning (IL), an agent aims to learn an optimal expert behavior policy
without additional online environment interactions. However, in many real-world scenarios …

Deep reinforcement learning (DRL) has demonstrated impressive performance in various
gaming simulators and real-world applications. In practice, however, a DRL agent may …

In offline Imitation Learning (IL), an agent aims to learn an optimal expert behavior policy
without additional online environment interactions. However, in many real-world scenarios …

Deep reinforcement learning (DRL) has demonstrated impressive performance in various
gaming simulators and real-world applications. In practice, however, a DRL agent may …

Causal dynamics learning has recently emerged as a promising approach to enhancing
robustness in reinforcement learning (RL). Typically, the goal is to build a dynamics model …

Machine learning models are often trained on data from one distribution and deployed on
others. So it becomes important to design models that are robust to distribution shifts. Most of …

Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes
domains such as healthcare, where direct deployment is often infeasible, unethical, or …