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October 10, 2019 19:53
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Forest management example
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import numpy as onp | |
def forest_management(forest_stages=3, r1=4, r2=2, p=0.1): | |
"""Forest management example from the MDPToolbox package. | |
Chadès, I., Chapron, G., Cros, M.‐J., Garcia, F. and Sabbadin, R. 2014. | |
MDPtoolbox: a multi‐platform toolbox to solve stochastic dynamic programming problems. | |
Ecography 37: 916–920 (ver. 0). | |
Args: | |
forest_stages (int, optional): Number of possibles states of the forest: from young to old. | |
Defaults to 3. | |
r1 (int, optional): Payoff for preserving an old forest. Defaults to 4. | |
r2 (int, optional): Payoff for deforesting an old forest. Defaults to 2. | |
p (float, optional): Probability of wildfire. Defaults to 0.1. | |
Returns: | |
tuple: (transition, reward, discount) | |
""" | |
nactions = 2 | |
transition = onp.zeros((nactions, forest_stages, forest_stages)) | |
onp.fill_diagonal(transition[0, :, 1:], 1-p) | |
transition[0, :, 0] = p | |
transition[0, -1, -1] = 1-p | |
transition[1, :, 0] = 1 | |
reward = onp.zeros((forest_stages, nactions)) | |
reward[-1, 0] = r1 | |
reward[:, 1] = 1 | |
reward[0, 1] = 0 | |
reward[-1, 1] = r2 | |
return transition, reward, 0.9 |
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