Sample Efficient Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to train effective decision-making agents using only pre-collected data, avoiding the need for costly and potentially risky online interaction with the environment. Current research focuses on improving sample efficiency—minimizing the amount of data needed for successful learning—through techniques like leveraging data diversity, exploiting inherent symmetries in system dynamics, and employing marginalized importance sampling with additional covering distributions. These advancements are crucial for making offline RL practical in real-world applications, such as traffic signal control, where extensive online data collection is infeasible or undesirable, and for addressing the fundamental limitations of existing algorithms in handling large state spaces and limited data coverage.