In this paper, we present an accelerating cosmological model by constraining the free parameters using datasets in extended symmetric teleparallel gravity for flat and anisotropic space-time. We employ a time variable deceleration parameter that behaves early late acceleration form of Hybrid Scale Factor (HSF). obtain values analyse behavior Universe based on best-fit parameters. derive dynamic...

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Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of user interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated ...

In this paper, we study applying Reinforcement Learning to design a automatic agent to play the game Super Mario Bros. One of the challenge is how to handle the complex game environment. By abstracting the game environment into a state vector and using Q learning — an algorithm oblivious to transitional probabilities — we achieve tractable computation time and fast convergence. After training f...

Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simula...