switchable NN/array

1 files changed, 12 insertions, 6 deletions

diff --git a/sol.py b/sol.py
index af35140..20e9fa9 100644
--- a/sol.py
+++ b/sol.py
@@ -48,10 +48,8 @@ print(arg)
 
 mode = None
 if arg['-h']:
-    print("Usage: %s MODE [OPTIONS]" % argv[0])
-    print("       MODE:  -1 / --policy-evaluation or\n" +
-          "              -2 / --q-learning\n" +
-          "       OPTIONS: --theta NUM     # convergence threshold\n" +
+    print("Usage: %s [OPTIONS]" % argv[0])
+    print("       OPTIONS: --theta NUM     # convergence threshold\n" +
           "                                # default: %f\n" % theta +
           "                --gamma NUM     # learning discount\n" +
           "                                # default: %f\n" % gamma +
@@ -66,6 +64,10 @@ if arg['-h']:
           "                                # default: %f\n" % epsilon +
           "                --epsred NUM    # reduction of epsilon per episode\n" +
           "                                # default: %f\n\n" % epsilon_reduction +
+          "                --qfunc TYPE    # type of the Q function's representation\n" +
+          "                                  arr / array -> plain standard array\n" +
+          "                                  nn          -> neural network representation\n" +
+          "                                  default: array" +
           "                --frameskip NUM # frameskip for visualisation\n" +
           "                                # default: %f\n" % frameskip +
           "                --quiet         # disable visualisation\n" +
@@ -249,8 +251,12 @@ class QNN:
         self.NN.train(list(s), [x/10. for x in newval])
 
 a = World(maze, start)
-#Q=QArray()
-Q=QNN()
+
+Q = None
+if arg['--qfunc'] == "nn":
+    Q = QNN()
+else:
+    Q = QArray()
 
 i=0
 stopstate = -1