### added option for sparse transition matrix to be generated from the exampleForest function

parent b475e0a0
 ... ... @@ -100,6 +100,7 @@ from numpy import absolute, array, diag, empty, matrix, mean, mod, multiply from numpy import ndarray, ones, zeros from numpy.random import rand from scipy.sparse import csr_matrix as sparse from scipy.sparse import coo_matrix # __all__ = ["check", "checkSquareStochastic"] ... ... @@ -371,15 +372,15 @@ def checkSquareStochastic(Z): return(None) def exampleForest(S=3, r1=4, r2=2, p=0.1): def exampleForest(S=3, r1=4, r2=2, p=0.1, is_sparse=False): """Generate a MDP example based on a simple forest management scenario. This function is used to generate a transition probability (A×S×S) array P and a reward (S×A) matrix R that model the following problem. A forest is managed by two actions: 'Wait' and 'Cut'. An action is decided each year with first the objective to maintain an old forest for wildlife and second to make money selling cut wood. Each year there is a probability ``p`` that a fire burns the forest. This function is used to generate a transition probability (``A`` × ``S`` × ``S``) array ``P`` and a reward (``S`` × ``A``) matrix ``R`` that model the following problem. A forest is managed by two actions: 'Wait' and 'Cut'. An action is decided each year with first the objective to maintain an old forest for wildlife and second to make money selling cut wood. Each year there is a probability ``p`` that a fire burns the forest. Here is how the problem is modelled. Let {1, 2 . . . ``S`` } be the states of the forest, with ``S`` being the ... ... @@ -471,12 +472,27 @@ def exampleForest(S=3, r1=4, r2=2, p=0.1): # | . . . | | . . . | # | . . 1-p | | . . . | # | p 0 0....0 1-p | | 1 0..........0 | P = zeros((2, S, S)) P[0, :, :] = (1 - p) * diag(ones(S - 1), 1) P[0, :, 0] = p P[0, S - 1, S - 1] = (1 - p) P[1, :, :] = zeros((S, S)) P[1, :, 0] = 1 if is_sparse: P = [{"row":[],"col":[],"val":[]}, {"row":[],"col":[],"val":[]}] P["row"] = range(S) * 2 P["col"] =  * S P["col"].extend(range(1, S)) P["col"].append(S - 1) P["val"] = [0.1] * S P["val"].extend([0.9] * S) P["row"] = range(S) P["col"] =  * S P["val"] =  * S for x in range(2): P[x] = coo_matrix((P[x]["val"], (P[x]["row"], P[x]["col"])), shape=(S,S)).tocsr() else: P = zeros((2, S, S)) P[0, :, :] = (1 - p) * diag(ones(S - 1), 1) P[0, :, 0] = p P[0, S - 1, S - 1] = (1 - p) P[1, :, :] = zeros((S, S)) P[1, :, 0] = 1 # Definition of Reward matrix R1 associated to action Wait and # R2 associated to action Cut # | 0 | | 0 | ... ...
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