diff options
Diffstat (limited to 'sol.py')
| -rw-r--r-- | sol.py | 23 |
1 files changed, 14 insertions, 9 deletions
@@ -280,32 +280,36 @@ class QNN (QCommon): self.dumbtraining = False connection_rate = 1 num_input = 2 - #hidden = (20,20) - hidden = (20,10,7) + #hidden = (40,40) + hidden = (50,) + #hidden = (20,10,7) num_output = 4 learning_rate = 0.7 self.NN = libfann.neural_net() #self.NN.set_training_algorithm(libfann.TRAIN_BATCH) - self.NN.set_training_algorithm(libfann.TRAIN_RPROP) + #self.NN.set_training_algorithm(libfann.TRAIN_RPROP) #self.NN.set_training_algorithm(libfann.TRAIN_QUICKPROP) self.NN.create_sparse_array(connection_rate, (num_input,)+hidden+(num_output,)) + self.NN.randomize_weights(-1,1) self.NN.set_learning_rate(learning_rate) 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): + #print ("eval "+str(x)+", "+str(y)) if y==None: x,y = x - return self.NN.run([x,y]) + #print ("self.NN.run("+str([x/7.,y/5.])+")") + return self.NN.run([x/7.,y/5.]) def change(self, s, action, diff): oldval = self.eval(s) newval = list(oldval) # copy list newval[action] += diff - self.NN.train(list(s), newval) + self.NN.train([s[0]/7.,s[1]/5.], newval) # learn a transition "from oldstate by action into newstate with reward `reward`" # this does not necessarily mean that the action is instantly trained into the function @@ -321,7 +325,8 @@ class QNN (QCommon): self.train_on_minibatch() def train_on_minibatch(self): - n = min(30, len(self.learnbuffer)) + n = min(300, len(self.learnbuffer)) + if n < 300: return minibatch = random.sample(self.learnbuffer, n) inputs = [] @@ -329,15 +334,15 @@ class QNN (QCommon): for oldstate, action, newstate, reward in minibatch: diff, val = self.value_update(oldstate, action, newstate, reward) val[action] += diff - inputs += [ list(oldstate) ] + inputs += [ [oldstate[0]/7., oldstate[1]/5.] ] outputs += [ val ] #print("training minibatch of size %i:\n%s\n%s\n\n"%(n, str(inputs), str(outputs))) training_data = libfann.training_data() training_data.set_train_data(inputs, outputs) - #self.NN.train_epoch(training_data) - self.NN.train_on_data(training_data, 5, 0, 0) + self.NN.train_epoch(training_data) + #self.NN.train_on_data(training_data, 5, 0, 0) #print(".") # must be called on every end-of-episode. might trigger batch-training or whatever. |