neuronal net. converges a bit. maybe. sometimes. slow.

1 files changed, 32 insertions, 1 deletions

diff --git a/sol.py b/sol.py
index fa1de97..cbccf67 100644
--- a/sol.py
+++ b/sol.py
@@ -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