In this paper we adopt general sum stochas tic games as a framework for multiagent re inforcement learning Our work extends pre vious work by Littman on zero sum stochas tic games to a broader framework We de sign a multiagent Q learning method under this framework and prove that it converges to a Nash equilibrium under speci ed condi tions This algorithm is useful for nding the optimal strateg...

New radiative-lifetime measurements based on time-resolved laser-induced fluorescence are reported for 133 odd-parity and 2 even-parity levels of Co I, ranging in energy from 28 300 to 59 400 cm–1. Our lifetimes agree with earlier, but much less extensive, lifetime measurements based on laser-induced fluorescence. Satisfactory agreement is also found with the critical compilation of atomic tran...

In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of the market clearing price. In this paper, a new market simulator is introduced for a joint energy and spinning reserve market, in which market participants’ learning process is modeled using Q-learnin...

Fermionic formulae originate in the Bethe ansatz in solvable lattice models. They are specific expressions of some q-polynomials as sums of products of q-binomial coefficients. We consider the fermionic formulae associated with general non-twisted quantum affine algebra Uq(X (1) n ) and discuss several aspects related to representation theories and combinatorics. They include crystal base theor...

Q-learning is a simple and powerful tool in solving dynamic problems where environments are unknown. It uses a balance of exploration and exploitation to find an optimal solution to the problem. In this paper, we propose using four basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent. Simulations show that the proposed affective agent requires lesser number of steps to f...

In this paper, we study phase transitions of the q-state Potts model through a number unsupervised machine learning techniques, namely Principal Component Analysis (PCA), k-means clustering, Uniform Manifold Approximation and Projection (UMAP), Topological Data (TDA). Even though in all cases are able to retrieve correct critical temperatures $$T_\textrm{c}(q)$$ , for $$q=3,4$$ 5, results show ...