Huazhong Wang

Toward Conservative Planning from Preferences in Offline Reinforcement Learning

We study offline reinforcement learning (RL) with trajectory preferences, where the RL agent does not receive explicit rewards at each step but instead receives human-provided preferences over pairs of trajectories. Despite growing interest in preference-based reinforcement learning (PbRL), contempo…