provide a command line option which prints the tuning_parameters to stdout

coach.py

@@ -273,6 +273,9 @@ if __name__ == "__main__":
                              "\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"",
                         default=None,
                         type=str)
+    parser.add_argument('--print_parameters',
+                        help="(flag) Print tuning_parameters to stdout",
+                        action='store_true')
 
     args, run_dict = check_input_and_fill_run_dict(parser)
 
@@ -290,6 +293,9 @@ if __name__ == "__main__":
         tuning_parameters = json_to_preset(json_run_dict_path)
         tuning_parameters.sess = set_framework(args.framework)
 
+        if args.print_parameters:
+            print('tuning_parameters', tuning_parameters)
+
         # Single-thread runs
         tuning_parameters.task_index = 0
         env_instance = create_environment(tuning_parameters)
@@ -352,5 +358,3 @@ if __name__ == "__main__":
         # wait for all workers
         [w.wait() for w in workers]
         evaluation_worker.kill()
-
-

configurations.py

@@ -16,7 +16,7 @@
 
 from utils import Enum
 import json
-from logger import screen, logger
+import types
 
 
 class Frameworks(Enum):
@@ -56,7 +56,24 @@ class MiddlewareTypes(object):
     FC = 2
 
 
-class AgentParameters(object):
+class Parameters(object):
+    def __str__(self):
+        parameters = {}
+        for k, v in self.__dict__.items():
+            if isinstance(v, type) and issubclass(v, Parameters):
+                # v.__dict__ doesn't return a dictionary but a mappingproxy
+                # which json doesn't serialize, so convert it into a normal
+                # dictionary
+                parameters[k] = dict(v.__dict__.items())
+            elif isinstance(v, types.MappingProxyType):
+                parameters[k] = dict(v.items())
+            else:
+                parameters[k] = v
+                
+        return json.dumps(parameters, indent=4, default=repr)
+
+
+class AgentParameters(Parameters):
     agent = ''
 
     # Architecture parameters
@@ -129,7 +146,7 @@ class AgentParameters(object):
     share_statistics_between_workers = True
 
 
-class EnvironmentParameters(object):
+class EnvironmentParameters(Parameters):
     type = 'Doom'
     level = 'basic'
     observation_stack_size = 4
@@ -143,7 +160,7 @@ class EnvironmentParameters(object):
     human_control = False
 
 
-class ExplorationParameters(object):
+class ExplorationParameters(Parameters):
     # Exploration policies
     policy = 'EGreedy'
     evaluation_policy = 'Greedy'
@@ -177,7 +194,7 @@ class ExplorationParameters(object):
     dt = 0.01
 
 
-class GeneralParameters(object):
+class GeneralParameters(Parameters):
     train = True
     framework = Frameworks.TensorFlow
     threads = 1
@@ -224,7 +241,7 @@ class GeneralParameters(object):
     test_num_workers = 1
 
 
-class VisualizationParameters(object):
+class VisualizationParameters(Parameters):
     # Visualization parameters
     record_video_every = 1000
     video_path = '/home/llt_lab/temp/breakout-videos'
@@ -587,4 +604,3 @@ class Preset(GeneralParameters):
         self.agent = agent
         self.env = env
         self.exploration = exploration
-