Upload README.md with huggingface_hub

README.md

@@ -11,16 +11,16 @@ model-index:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: doom_health_gathering
-      type: doom_health_gathering
+      name: doom_health_gathering_supreme
+      type: doom_health_gathering_supreme
     metrics:
     - type: mean_reward
-      value: 21.00 +/- 0.00
+      value: 8.07 +/- 1.90
       name: mean_reward
       verified: false
 ---
 
-A(n) **APPO** model trained on the **doom_health_gathering** environment.
+A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
 
 This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
 Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r edbeeching/rl_course_vizdo
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
+python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_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 .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
+python -m .usr.local.lib.python3.8.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.