ikostrikov#109 Add type to discount factor (tau) in target network up…

…date argument

a2c_ppo_acktr/arguments.py

@@ -156,7 +156,7 @@ def get_args(sysargs):
     parser.add_argument('--learning.consistency_loss.target-model-update-frequency', default=-1,
                         help="If set to value > 0, the target model in the consistency loss will be updated with given "
                              "frequency")
-    parser.add_argument('--learning.consistency_loss.target-model-discount', default=1.0,
+    parser.add_argument('--learning.consistency_loss.target-model-discount', default=1.0, type=float,
                         help="Discount of 1.0: use new weights for target model. Discount < 1.0: exponential moving avg.")
 
     # experiment

a2c_ppo_acktr/utils.py

@@ -87,9 +87,9 @@ def update_state_dict(model, state_dict, tau=1.0):
     ``tau==1`` applies hard update, copying the values, ``0<tau<1``
     applies soft update: ``tau * new + (1 - tau) * old``.
     """
-    if tau == 1:
+    if tau == 1.0:
         model.load_state_dict(state_dict)
-    elif tau > 0:
-        update_sd = {k: tau * state_dict[k] + (1 - tau) * v
+    elif tau > 0.0:
+        update_sd = {k: tau * state_dict[k] + (1.0 - tau) * v
                      for k, v in model.state_dict().items()}
         model.load_state_dict(update_sd)

main.py

@@ -138,7 +138,12 @@ def setup_dirs_and_logging(cfg: SimpleNamespace):
     # curriculum_log_file = open(os.path.join(cfg.log_dir, "curriculum_log.json"), 'w')
 
     # Copy original configuration if present to new location
-    shutil.copyfile(str(cfg.config[0]), os.path.join(cfg.log_dir, "config.yaml"))
+    shutil.copyfile(str(cfg.config[0]), os.path.join(cfg.log_dir, "config_original.yaml"))
+
+    with open(os.path.join(cfg.log_dir, "config.yaml"), 'w+') as file:
+        yaml.dump(vars(cfg), file)
+
+
 
     return tb_writer, tb_writer_img, log_file