In this paper, the cosmological model of Universe has been presented in f ( Q ) $f(Q)$ gravity and parameters are constrained from data sets. At beginning, a well motivated form = α + β n $f(Q) \alpha \beta Q^{n}$ employed, where α, β, parameters. The Hubble parameter is obtained redshift with some algebraic manipulation considered . Then it parameterized recent $\text{Hubble}$ Pantheon SHOES $...

Coupled multi-component $\mathbb{C}P^N$ models with V-shaped potentials are analyzed. It is shown that the model has solutions being combinations of compact Q-balls and Q-shells. The nature permits existence novel harbor-type having form sheltered by relation between energy $E$ Noether charge $Q$ discussed both analytically numerically. behaves as $E\sim |Q|^\alpha,~\alpha<1$, i.e., for standar...

Coco (“cooperative/competitive”) values are a solution concept for two-player normalform games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that coco values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing ...

In this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one defined in a different region of the domain. Associated with each approximator is a relevance function that locally quantifies the quality of its approximation, so that, at each...

Abstract. Recent work has applied the Markov Game formalism from AI to model game dynamics for ice hockey, using a large state space. Dynamic programming is used to learn action-value functions that quantify the impact of actions on goal scoring. Learning is based on a massive dataset that contains over 2.8M events in the National Hockey League. As an application of the Markov model, we use the...

We extend Q-learning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learn...

A reinforcement architecture is introduced that consists of three complementary learning systems with different generalization abilities. The ACTOR learns state-action associations, the CRITIC learns a goal-gradient, and the PUNISH system learns what actions to avoid. The architecture is compared to the standard actor-crititc and Q-learning models on a number of maze learning tasks. The novel a...

We study the simplest model of dynamic heterogeneities in glass forming liquids: one-spin facilitated kinetic Ising model introduced by Fredrickson and Andersen [G.H. Fredrickson and H.C. Andersen, Phys. Rev. Lett. 53, 1244 (1984); J. Chem. Phys. 83, 5822 (1985)]. We show that the low-temperature, long-time behavior of the density autocorrelation function predicted by a scaling approach can be ...