Game theory - compute the Nash equilibrium for the given zero-sum games using linear programming such as CVXOPT
Python
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Unassigned
schedule
5 Days
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$1.00-$70.00 you are asked to compute the Nash equilibrium for the given zero-sum
games. You will be given the reward matrix for Player A. Player B’s reward matrix will be the
opposite of Player A’s matrix. You need to find the ideal mixed strategy for the game. While there
are different ways to calculate this we will use Linear Programming in hopes of preparing you for
your final project. Use a Linear Programming solver -- such as CVXOPT -- to create a program
that can solve arbitrary Rock, Paper, Scissor games.
You will submit the objective, constraints and strategy for your Linear Prog
input 1 - [[0.0, 4.82, -4.88], [-4.82, 0.0, 1.0], [4.88, -1.0, 0.0]]
input 2 - [[0.0, 1.19, -0.93], [-1.19, 0.0, 1.0], [0.93, -1.0, 0.0]]
input 3 - [[0.0, 3.84, -1.0], [-3.84, 0.0, 1.0], [1.0, -1.0, 0.0]]
input 4 - [[0.0, 1.0, -3.51], [-1.0, 0.0, 2.36], [3.51, -2.36, 0.0]]
input 5 - [[0.0, 0.77, -1.14], [-0.77, 0.0, 2.5], [1.14, -2.5, 0.0]]
input 6 - [[0.0, 1.46, -1.0], [-1.46, 0.0, 4.18], [1.0, -4.18, 0.0]]
input 7 - [[0.0, 4.89, -2.22], [-4.89, 0.0, 1.0], [2.22, -1.0, 0.0]]
input 8 - [[0.0, 2.06, -2.5], [-2.06, 0.0, 1.0], [2.5, -1.0, 0.0]]
input 9 - [[0.0, 1.0, -0.45], [-1.0, 0.0, 0.91], [0.45, -0.91, 0.0]]
input 10 - [[0.0, 1.0, -1.0], [-1.0, 0.0, 0.49], [1.0, -0.49, 0.0]]
Sample Solution