Fix README

README.md

@@ -154,13 +154,13 @@ python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-last-ch
 
 Upload model to hub (same syntax as for `enjoy.py`):
 ```
-python -m utils.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3 -m "Initial commit"
+python -m rl_zoo.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3 -m "Initial commit"
 ```
 you can choose custom `repo-name` (default: `{algo}-{env_id}`) by passing a `--repo-name` argument.
 
 Download model from hub:
 ```
-python -m utils.load_from_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
+python -m rl_zoo.load_from_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
 ```
 
 ## Hyperparameter yaml syntax
@@ -255,7 +255,7 @@ for multiple, specify a list:
 
 ```yaml
 env_wrapper:
-    - utils.wrappers.DoneOnSuccessWrapper:
+    - rl_zoo.wrappers.DoneOnSuccessWrapper:
         reward_offset: 1.0
     - sb3_contrib.common.wrappers.TimeFeatureWrapper
 ```
@@ -279,7 +279,7 @@ Following the same syntax as env wrappers, you can also add custom callbacks to
 
 ```yaml
 callback:
-  - utils.callbacks.ParallelTrainCallback:
+  - rl_zoo.callbacks.ParallelTrainCallback:
       gradient_steps: 256
 ```
 
@@ -306,19 +306,19 @@ Note: if you want to pass a string, you need to escape it like that: `my_string:
 Record 1000 steps with the latest saved model:
 
 ```
-python -m utils.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000
+python -m rl_zoo.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000
 ```
 
 Use the best saved model instead:
 
 ```
-python -m utils.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-best
+python -m rl_zoo.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-best
 ```
 
 Record a video of a checkpoint saved during training (here the checkpoint name is `rl_model_10000_steps.zip`):
 
 ```
-python -m utils.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-checkpoint 10000
+python -m rl_zoo.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-checkpoint 10000
 ```
 
 ## Record a Video of a Training Experiment
@@ -328,18 +328,18 @@ Apart from recording videos of specific saved models, it is also possible to rec
 Record 1000 steps for each checkpoint, latest and best saved models:
 
 ```
-python -m utils.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic
+python -m rl_zoo.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic
 ```
 
 The previous command will create a `mp4` file. To convert this file to `gif` format as well:
 
 ```
-python -m utils.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic --gif
+python -m rl_zoo.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic --gif
 ```
 
 ## Current Collection: 195+ Trained Agents!
 
-Final performance of the trained agents can be found in [`benchmark.md`](./benchmark.md). To compute them, simply run `python -m utils.benchmark`.
+Final performance of the trained agents can be found in [`benchmark.md`](./benchmark.md). To compute them, simply run `python -m rl_zoo.benchmark`.
 
 List and videos of trained agents can be found on our Huggingface page: https://huggingface.co/sb3
 

rl_zoo/record_training.py

@@ -79,19 +79,19 @@
         args_final_model.append("--deterministic")
 
     if os.path.exists(os.path.join(log_path, f"{env_name}.zip")):
-        return_code = subprocess.call(["python", "-m", "utils.record_video"] + args_final_model)
+        return_code = subprocess.call(["python", "-m", "rl_zoo.record_video"] + args_final_model)
         assert return_code == 0, "Failed to record the final model"
 
     if os.path.exists(os.path.join(log_path, "best_model.zip")):
         args_best_model = args_final_model + ["--load-best"]
-        return_code = subprocess.call(["python", "-m", "utils.record_video"] + args_best_model)
+        return_code = subprocess.call(["python", "-m", "rl_zoo.record_video"] + args_best_model)
         assert return_code == 0, "Failed to record the best model"
 
     args_checkpoint = args_final_model + ["--load-checkpoint"]
     args_checkpoint.append("0")
     for checkpoint in checkpoints:
         args_checkpoint[-1] = str(checkpoint)
-        return_code = subprocess.call(["python", "-m", "utils.record_video"] + args_checkpoint)
+        return_code = subprocess.call(["python", "-m", "rl_zoo.record_video"] + args_checkpoint)
         assert return_code == 0, f"Failed to record the {checkpoint} checkpoint model"
 
     # add text to each video