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README.md

@@ -15,7 +15,7 @@ model-index:
       type: doom_health_gathering_supreme
     metrics:
     - type: mean_reward
-      value: 3.94 +/- 0.61
+      value: 11.60 +/- 5.01
       name: mean_reward
       verified: false
 ---
@@ -38,19 +38,19 @@ python -m sample_factory.huggingface.load_from_hub -r RajkNakka/rl_course_vizdoo
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
+python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
 ```
 
 
 You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
 See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
-    
+        
 ## Training with this model
 
 To continue training with this model, use the `train` script corresponding to this environment:
 ```
-python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
+python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
 ```
 
 Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
-    
+