made action scaling a parameter

cleanrl/dqn.py

@@ -39,8 +39,6 @@ def parse_args():
     parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
         help="whether to save model into the `runs/{run_name}` folder")
     parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
-        help="whether to upload the saved model to huggingface")
-    parser.add_argument("--hf-entity", type=str, default="",
         help="the user or org name of the model repository from the Hugging Face Hub")
 
     # Algorithm specific arguments
@@ -52,6 +50,8 @@ def parse_args():
         help="how many violations before you terminate")
     parser.add_argument("--failure-penalty", type=float, default=0.0,
         help="Reward Penalty when you fail")
+    parser.add_argument("--action-scale", type=float, default=0.2,
+        help="Reward Penalty when you fail")    
     parser.add_argument("--collect-data", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
         help="store data while trianing")
     parser.add_argument("--storage-path", type=str, default="./data/ppo/term_1",
@@ -126,13 +126,6 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
 
 
 
-action_map = {0: np.array([1., 1.]), 1: np.array([0., 1.]), 2: np.array([1., 0.])}
-def action_map_fn(action):
-    return action_map[action]
-
-def get_random_action():
-    return random.choice(range(len(action_map)))
-
 
 
 if __name__ == "__main__":
@@ -147,6 +140,14 @@ def get_random_action():
         )
     args = parse_args()
     run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    action_map = {0: np.array([args.action_scale, args.action_scale]), 1: np.array([0., args.action_scale]), 2: np.array([args.action_scale, 0.])}
+    def action_map_fn(action):
+        return action_map[action]
+
+    def get_random_action():
+        return random.choice(range(len(action_map)))
+
+
     if args.track:
         import wandb