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March 16, 2025
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========1======================== Sure! Your request is to analyze the comprehensive performance of Hideo Kojima’s Metal Gear series on PlayStation platforms over time, calculate a weighted performance index (emphasizing fluidity, with stability, SF precision, and dynamic SF included), and plot it using MATLAB with the Y-axis as the index and the X-axis as time. Below, I’ll… read more »
Matlab Code: club0 = {‘barcelona’ ‘bayern’ ‘realmadrid’ ‘manunited’ ‘liverpool’,‘mancity’,‘inter’,‘juventus’}; ll = {‘Barcelona’,‘Bayern’,‘Real Madrid’,‘Man United’,‘Liverpool’,‘Man City’,‘Inter’,‘Juventus’}; start = ’01-Jan-2020′; D = ‘./’; for i_club = 1:length(club0);club = club0{i_club}; url = sprintf(‘http://api.clubelo.com/%s’,club); % filename = sprintf(‘%sdata%d.csv’,D,i_club); % websave(filename, url); end S = dir(fullfile(D,‘data*.csv’)); datatotal = cell(1,length(club0)); for k = 1:numel(S) F = fullfile(D,S(k).name); datatotal{k} = readtable(F); end… read more »
As semiconductor technology advances, gaming platform hardware accelerates to meet the increasing demands of software, enhancing user interaction and enriching entertainment experiences for the general public. For example, popular video games like “Metal Gear” showcase the improvements in graphics and gameplay made possible by these advancements, allowing players to immerse themselves in more dynamic and… read more »
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms finite-sample convergence rates for q-learning and indirect algorithms
Solving H-horizon, Stationary Markov Decision Problems In Time Proportional To Log(H) Solving h-horizon, stationary markov decision problems in time proportional to log (h) Paul Tseng, Operations Reseserch Letters 9 (1990) 287-297.
Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Run Time Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Run Time The nonlinear Bellman equation = linear programming problem: Primal-Dual LP Primal LP (1) Dual LP (2) Minmax Problem (3) Download: pdf
KL Divergence In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy) is a measure of how one probability distribution is different from a second, reference probability distribution. https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence Information entropy KL Divergence
The Asymptotic Convergence-Rate of Q-learning the-asymptotic-convergence-rate-of-q-learning The asymptotic rate of convergence of Q-learning is Ο( 1/tR(1-γ) ), if R(1-γ)<0.5, where R=Pmin/Pmax, P is state-action occupation frequency. |Qt (x,a) − Q*(x,a)| < B/tR(1-γ) Convergence-rate is the difference between True value and Optimum value, i.e., the smaller it is, the faster convergence Q-learning is. We hope the Ο( 1/tR(1-γ) ) should… read more »
Policy Gradient Methods In summary, I guess because 1. policy (probability of action) has the style: , 2. obtain (or let’s say ‘math trick’) in the objective function ( i.e., value function )’s gradient equation to get an ‘Expectation’ form for : , assign ‘ln’ to policy before gradient for analysis convenience. pg Notation J(θ):… read more »