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README.md

@@ -15,7 +15,7 @@ model-index:
       type: doom_health_gathering_supreme
     metrics:
     - type: mean_reward
-      value: 11.23 +/- 6.24
+      value: 18.61 +/- 4.52
       name: mean_reward
       verified: false
 ---
@@ -30,7 +30,7 @@ Documentation for how to use Sample-Factory can be found at https://www.samplefa
 
 After installing Sample-Factory, download the model with:
 ```
-python -m sample_factory.huggingface.load_from_hub -r eolang/rl_course_vizdoom_health_gathering_supreme
+python -m sample_factory.huggingface.load_from_hub -r eolang/DRL-vizdoome_health_gathering_supreme
 ```
 
     
@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r eolang/rl_course_vizdoom_h
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
+python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_health_gathering_supreme
 ```
 
 
@@ -49,7 +49,7 @@ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
 
 To continue training with this model, use the `train` script corresponding to this environment:
 ```
-python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-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
+python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_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.