evaluation adds custom agent resolution

examples/submission/agent.py

@@ -9,13 +9,17 @@ class Agent(object):
         """
          Load your agent here and initialize anything needed
         """
-
+        # You can specify the resolution your agent takes as input, for example set resolution=128 to
+        # have visual inputs of size 128*128*3 (if this attribute is omitted it defaults to 84)
+        
+        print('Resoluton {}'.format(self.resolution))
         # Load the configuration and model using ABSOLUTE PATHS
         self.configuration_file = '/aaio/data/trainer_config.yaml'
         self.model_path = '/aaio/data/1-Food/Learner'
 
         self.brain = BrainParameters(brain_name='Learner',
-                                     camera_resolutions=[{'height': 84, 'width': 84, 'blackAndWhite': False}],
+                                     camera_resolutions=[
+                                         {'height': self.resolution, 'width': self.resolution, 'blackAndWhite': False}],
                                      num_stacked_vector_observations=1,
                                      vector_action_descriptions=['', ''],
                                      vector_action_space_size=[3, 3],

examples/submission/test_submission/testDocker.py

@@ -26,6 +26,15 @@ def main():
         print('Your agent could not be reset:')
         raise e
 
+    try:
+        resolution = submitted_agent.resolution
+        assert isinstance(resolution, int)
+    except AttributeError:
+        resolution = 84
+    except AssertionError:
+        print('Resolution must be an integer')
+        return
+
     env = AnimalAIEnv(
         environment_filename='/aaio/test/env/AnimalAI',
         seed=0,
@@ -33,6 +42,7 @@ def main():
         n_arenas=1,
         worker_id=1,
         docker_training=True,
+        resolution=resolution
     )
 
     env.reset(arenas_configurations=arena_config_in)