A Python implementation of Fuzzy Q-Learning (FQL) for any controllers with continues states. As an example, the Pole Balancing problem (example/cartpole.py) has been implemented and it is inside this package.
Fuzzy Q-Learning is a fuzzy extension of Q-learning algorithm. For creating an FQL model you first need to specify the input states and their corresponding fuzzy sets and then build your Fuzzy Inference System (FIS) to integrate with the Q-Learning algorithm. In this code, two type of fuzzy membership functions have been implemented : 1) Triangular and 2) Trapezoidal. For more information please read the following publications:
Masoumzadeh, S. S., Hlavacs, H., & Tomás, L. (2016, September). A Self-Adaptive Performance-Aware Capacity Controller in Overbooked Datacenters. In Cloud and Autonomic Computing (ICCAC), 2016 International Conference on (pp. 12-23). IEEE.
Jouffe, L. (1998). Fuzzy inference system learning by reinforcement methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 338-355.
You can create fuzzy sets (as many as needed), initialise them, and assign them to an input state variable to build your FIS as follows:
from fuzzy_inference import fis, fuzzyset, state_variable
x1 = state_variable.InputStateVariable(fuzzyset.Trapeziums(-2.4, -2, -1, -0.5), fuzzyset.Trapeziums(-1, -0.5, 0.5 , 1), FuzzySet.Trapeziums(0.5, 1, 2, 2.4) )
x2 = state_variable.InputStateVariable(fuzzyset.Triangles(-2.4,-0.5,1), fuzzyset.Triangles(-0.5,1,2.4))
my_fis = fis.Build(x1, x2)
Then you can buid your FQL model as follow:
model = FQL.Model(gamma = 0.9, alpha = 0.1 , ee_rate = 0.999, q_initial_value = 'zero', action_set_length = 21, fis = my_fis)
You just need to change Environment class inside the code to create your reward function, apply the actions to the environment and getting new states.
The code is published under the [MIT License]