Hyperparameter Selection for Offline Reinforcement Learning

Understand

Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios.

• However, existing hyperparameter selection methods for offline RL break the offline assumption by evaluating policies corresponding to each hyperparameter setting in the environment.
• This online execution is often infeasible and hence undermines the main aim of offline RL.
• Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i.e.