Medium-range weather forecasts play a crucial role in agriculture, construction, travel and other industries. They also bring practical value to people's daily lives, enabling us to plan outings and keeping us safe from extreme weather events.

Recently, researchers from DeepMind and Google introduced methods for choosing the best policy in offline reinforcement learning (ORL) known as offline hyperparameter selection (OHS). It uses logged data from a set of many policies that are trained using different hyperparameters. Reinforcement learning has become one of the most critical techniques in AI which has been used to attain Artificial General Intelligence. Offline reinforcement learning has now become a fundamental approach for deploying RL techniques in real-world scenarios. According to this blog post, offline reinforcement learning can assist in pre-training a reinforcement learning agent using the existing data.