diff options
| -rw-r--r-- | sol.py | 33 |
1 files changed, 32 insertions, 1 deletions
@@ -2,6 +2,7 @@ from random import random from itertools import product from collections import defaultdict from sys import argv +from fann2 import libfann maze = [ [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], @@ -218,8 +219,38 @@ class QArray: self.Q[s[1]][s[0]][action] += diff +# implements the Q function not through an array, but through a neuronal network instead. +class QNN: + def __init__(self): + connection_rate = 1 + num_input = 2 + hidden = (50,50) + num_output = 4 + learning_rate = 0.7 + + self.NN = libfann.neural_net() + self.NN.create_sparse_array(connection_rate, (num_input,)+hidden+(num_output,)) + self.NN.set_learning_rate(learning_rate) + #self.NN.set_activation_function_input(libfann.SIGMOID_SYMMETRIC_STEPWISE) + self.NN.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE) + self.NN.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE) + #self.NN.set_activation_function_output(libfann.LINEAR) + + def eval(self,x,y = None): + if y==None: x,y = x + + return [x*10. for x in self.NN.run([x,y])] + + def change(self, s, action, diff): + oldval = self.eval(s) + newval = list(oldval) # copy list + newval[action] += diff + + self.NN.train(list(s), [x/10. for x in newval]) + a = World(maze, start) -Q=QArray() +#Q=QArray() +Q=QNN() i=0 stopstate = -1 |