Chengchun Shi

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing

Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm,...

Semi-pessimistic Reinforcement Learning

Offline reinforcement learning aims to learn an optimal policy from pre-collected data. However, it faces challenges of distributional shift, where the learned policy may encounter unseen scenarios not covered in the offline data. Additionally, numerous applications suffer from a...