원숭이투자 실행 개선

Author: quantylab

agent.py

@@ -65,8 +65,10 @@ def reset(self):
         self.ratio_hold = 0
         self.ratio_portfolio_value = 0
 
-    def reset_exploration(self):
-        self.exploration_base = 0.5 + np.random.rand() / 2
+    def reset_exploration(self, alpha=None):
+        if alpha is None:
+            alpha = np.random.rand() / 2
+        self.exploration_base = 0.5 + alpha
 
     def set_balance(self, balance):
         self.initial_balance = balance

learners.py

@@ -287,6 +287,7 @@ def run(
                 self.agent.reset_exploration()
             else:
                 epsilon = start_epsilon
+                self.agent.reset_exploration(alpha=0)
 
             while True:
                 # 샘플 생성

main.py

@@ -14,9 +14,9 @@
     parser.add_argument('--stock_code', nargs='+')
     parser.add_argument('--ver', choices=['v1', 'v2'], default='v2')
     parser.add_argument('--rl_method', 
-        choices=['dqn', 'pg', 'ac', 'a2c', 'a3c'])
+        choices=['dqn', 'pg', 'ac', 'a2c', 'a3c', 'monkey'])
     parser.add_argument('--net', 
-        choices=['dnn', 'lstm', 'cnn'], default='dnn')
+        choices=['dnn', 'lstm', 'cnn', 'monkey'], default='dnn')
     parser.add_argument('--num_steps', type=int, default=1)
     parser.add_argument('--lr', type=float, default=0.01)
     parser.add_argument('--discount_factor', type=float, default=0.9)
@@ -63,8 +63,8 @@
 
     # 로그, Keras Backend 설정을 먼저하고 RLTrader 모듈들을 이후에 임포트해야 함
     from agent import Agent
-    from learners import DQNLearner, PolicyGradientLearner, \
-        ActorCriticLearner, A2CLearner, A3CLearner
+    from learners import ReinforcementLearner, DQNLearner, \
+        PolicyGradientLearner, ActorCriticLearner, A2CLearner, A3CLearner
 
     # 모델 경로 준비
     value_network_path = ''
@@ -130,6 +130,13 @@
                 learner = A2CLearner(**{**common_params, 
                     'value_network_path': value_network_path, 
                     'policy_network_path': policy_network_path})
+            elif args.rl_method == 'monkey':
+                args.net = args.rl_method
+                args.num_epoches = 1
+                args.discount_factor = None
+                args.start_epsilon = 1
+                args.learning = False
+                learner = ReinforcementLearner(**common_params)
             if learner is not None:
                 learner.run(balance=args.balance, 
                     num_epoches=args.num_epoches, 

networks.py

@@ -23,7 +23,9 @@ def set_session(sess): pass
     from tensorflow.keras.backend import set_session
     import tensorflow as tf
     graph = tf.get_default_graph()
-    sess = tf.compat.v1.Session()
+    config = tf.compat.v1.ConfigProto()
+    config.gpu_options.allow_growth = True
+    sess = tf.compat.v1.Session(config=config)
 elif os.environ['KERAS_BACKEND'] == 'plaidml.keras.backend':
     from keras.models import Model
     from keras.layers import Input, Dense, LSTM, Conv2D, \