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In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from T T H project which consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume M dark-matter only simulation (MDPL2). We use as target variables a set of baryonic properties for the intra cluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the dark matter halos from the MDPL2 N-body simulation. The different ML models are trained from this database and subsequently used to infer the same baryonic properties for the whole range of cluster-size halos identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the dark matter halos and conclude that their inferred baryonic properties are rather insensitive to their DM properties which are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size halos with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly available.

The Whittle index policy is a heuristic that has shown remarkable good performance (with guaranted asymptotic optimality) when applied to the class of problems known as multi-armed restless bandits. In this paper we develop QWI, an algorithm based on Q-learning in order to learn theWhittle indices. The key feature is the deployment of two timescales, a relatively faster one to update the state-action Qfunctions, and a relatively slower one to update the Whittle indices. In our main result, we show that the algorithm converges to the Whittle indices of the problem. Numerical computations show that our algorithm converges much faster than both the standard Q-learning algorithm as well as neural-network based approximate Q-learning.