Rename experiment_name to experiment

README.md

@@ -53,12 +53,12 @@ If you run into issues, try starting the sim directly as Unreal may need to inst
 
 Run the **baseline** agent
 ```
-python main.py --baseline --experiment-name my-baseline-test
+python main.py --baseline --experiment my-baseline-test
 ```
 
 Run in-game path follower
 ```
-python main.py --path-follower --experiment-name my-path-follower-test
+python main.py --path-follower --experiment my-path-follower-test
 ```
 
 **Record** training data for imitation learning / behavioral cloning

agents/bootstrap_rl/train/train.py

@@ -103,7 +103,7 @@ def run(env_id, bootstrap_net_path,
         sess_1 = tf.Session(config=tf_config)
 
         with sess_1.as_default():
-            dagger_gym_env = deepdrive.start(experiment_name=experiment, env_id=env_id, cameras=camera_rigs, render=render, fps=fps,
+            dagger_gym_env = deepdrive.start(experiment=experiment, env_id=env_id, cameras=camera_rigs, render=render, fps=fps,
                                              combine_box_action_spaces=True, is_sync=is_sync,
                                              driving_style=driving_style, is_remote_client=is_remote_client)
 

api/server.py

@@ -52,7 +52,7 @@ def run(self):
                     resp = 'No environment started, please send start request'
                     log.error('Client sent request with no environment started')
                 elif method == m.START:
-                    allowed_args = ['experiment_name', 'env', 'cameras', 'combine_box_action_spaces', 'is_discrete',
+                    allowed_args = ['experiment', 'env', 'cameras', 'combine_box_action_spaces', 'is_discrete',
                                     'preprocess_with_tensorflow', 'is_sync']
                     if c.IS_EVAL:
                         allowed_args.remove('env')

deepdrive.py

@@ -21,7 +21,7 @@
 log = logs.get_log(__name__)
 
 def start(**kwargs):
-    all_kwargs = dict(experiment_name=None, env_id='Deepdrive-v0', sess=None, start_dashboard=True,
+    all_kwargs = dict(experiment=None, env_id='Deepdrive-v0', sess=None, start_dashboard=True,
                       should_benchmark=True, cameras=None, use_sim_start_command=False, render=False,
                       fps=c.DEFAULT_FPS, combine_box_action_spaces=False, is_discrete=False,
                       preprocess_with_tensorflow=False, is_sync=False, driving_style=DrivingStyle.NORMAL,
@@ -46,8 +46,8 @@ def start(**kwargs):
         env = gym.make(kwargs['env_id'])
         env.seed(c.RNG_SEED)
 
-        if kwargs['experiment_name'] is None:
-            kwargs['experiment_name'] = ''
+        if kwargs['experiment'] is None:
+            kwargs['experiment'] = ''
 
         deepdrive_env = env.unwrapped
 
@@ -59,7 +59,7 @@ def start(**kwargs):
         deepdrive_env.reset_returns_zero = kwargs['reset_returns_zero']
         deepdrive_env.init_action_space()
         deepdrive_env.fps = kwargs['fps']
-        deepdrive_env.experiment = kwargs['experiment_name'].replace(' ', '_')
+        deepdrive_env.experiment = kwargs['experiment'].replace(' ', '_')
         deepdrive_env.period = deepdrive_env.sync_step_time = 1. / kwargs['fps']
         deepdrive_env.driving_style = kwargs['driving_style']
         deepdrive_env.should_render = kwargs['render']

main.py

@@ -78,7 +78,7 @@ def main():
     parser.add_argument('-v', '--verbose', help='Increase output verbosity',
                         action='store_true')
     parser.add_argument('--camera-rigs', nargs='?', default=None, help='Name of camera rigs to use')
-    parser.add_argument('-n', '--experiment-name', nargs='?', default=None, help='Name of your experiment')
+    parser.add_argument('--experiment', nargs='?', default=None, help='Name of your experiment')
     parser.add_argument('--fps', type=int, default=c.DEFAULT_FPS, help='Frames / steps per second')
     parser.add_argument('--agent', nargs='?', default=c.DAGGER_MNET2,
                         help='Agent type (%s, %s, %s)' % (c.DAGGER,
@@ -118,7 +118,7 @@ def main():
 
 def run_agent(args, camera_rigs, driving_style):
     from agents.dagger import agent
-    agent.run(args.experiment_name,
+    agent.run(args.experiment,
               should_record=args.record, net_path=args.net_path, env_id=args.env_id,
               run_baseline_agent=args.baseline, run_mnet2_baseline_agent=args.mnet2_baseline,
               run_ppo_baseline_agent=args.ppo_baseline, render=args.render, camera_rigs=camera_rigs,
@@ -138,7 +138,7 @@ def run_path_follower(args, driving_style, camera_rigs):
         cams = camera_rigs
         if isinstance(camera_rigs[0], list):
             cams = cams[0]
-        gym_env = deepdrive.start(experiment_name=args.experiment_name, env_id=args.env_id, fps=args.fps,
+        gym_env = deepdrive.start(experiment=args.experiment, env_id=args.env_id, fps=args.fps,
                                   driving_style=driving_style, is_remote_client=args.is_remote_client,
                                   render=args.render, cameras=cams)
         log.info('Path follower drive mode')