This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to ...

Abstract: Offline reinforcement learning (RL) aims to learn the possible policy from a fixed dataset without real-time interactions with the environment. By avoiding the risky exploration of the robot ...