update DQN hyperparam

examples/Atari2600/DQN.py

@@ -20,11 +20,12 @@ from tensorpack.RL import *
 import common
 from common import play_model, Evaluator, eval_model_multithread
 
-BATCH_SIZE = 32
+BATCH_SIZE = 64
 IMAGE_SIZE = (84, 84)
 FRAME_HISTORY = 4
 ACTION_REPEAT = 4
-HEIGHT_RANGE = (36, 204)    # for breakout
+HEIGHT_RANGE = (None, None)
+#HEIGHT_RANGE = (36, 204)    # for breakout
 #HEIGHT_RANGE = (28, -8)   # for pong
 
 CHANNEL = FRAME_HISTORY
@@ -32,7 +33,7 @@ IMAGE_SHAPE3 = IMAGE_SIZE + (CHANNEL,)
 GAMMA = 0.99
 
 INIT_EXPLORATION = 1
-EXPLORATION_EPOCH_ANNEAL = 0.008
+EXPLORATION_EPOCH_ANNEAL = 0.01
 END_EXPLORATION = 0.1
 
 MEMORY_SIZE = 1e6
@@ -133,7 +134,7 @@ class Model(ModelDesc):
                 SummaryGradient()]
 
     def predictor(self, state):
-        # TODO change to a multitower predictor for speedup
+        # TODO use multitower predictor to speed up training
         return self.predict_value.eval(feed_dict={'state:0': [state]})[0]
 
 def get_config():
@@ -155,7 +156,7 @@ def get_config():
             reward_clip=(-1, 1),
             history_len=FRAME_HISTORY)
 
-    lr = tf.Variable(0.0004, trainable=False, name='learning_rate')
+    lr = tf.Variable(0.001, trainable=False, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
     return TrainConfig(
@@ -164,11 +165,13 @@ def get_config():
         callbacks=Callbacks([
             StatPrinter(),
             ModelSaver(),
+            ScheduledHyperParamSetter('learning_rate',
+                [(150, 4e-4), (250, 1e-4), (350, 5e-5)]),
             HumanHyperParamSetter('learning_rate', 'hyper.txt'),
             HumanHyperParamSetter(ObjAttrParam(dataset_train, 'exploration'), 'hyper.txt'),
             RunOp(lambda: M.update_target_param()),
             dataset_train,
-            PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['fct/output']), 2),
+            PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['fct/output']), 3),
         ]),
         # save memory for multiprocess evaluator
         session_config=get_default_sess_config(0.6),
@@ -205,6 +208,6 @@ if __name__ == '__main__':
         if args.load:
             config.session_init = SaverRestore(args.load)
         SimpleTrainer(config).train()
+        # TODO test if queue trainer works
         #QueueInputTrainer(config).train()
-        # TODO test if QueueInput affects learning