Upload README.md with huggingface_hub

README.md

@@ -1,37 +1,56 @@
 ---
-library_name: stable-baselines3
+library_name: sample-factory
 tags:
-- PandaReachDense-v3
 - deep-reinforcement-learning
 - reinforcement-learning
-- stable-baselines3
+- sample-factory
 model-index:
-- name: A2C
+- name: APPO
   results:
   - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: PandaReachDense-v3
-      type: PandaReachDense-v3
+      name: doom_health_gathering_supreme
+      type: doom_health_gathering_supreme
     metrics:
     - type: mean_reward
-      value: -0.21 +/- 0.08
+      value: 9.92 +/- 2.85
       name: mean_reward
       verified: false
 ---
 
-# **A2C** Agent playing **PandaReachDense-v3**
-This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
 
-## Usage (with Stable-baselines3)
-TODO: Add your code
+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/
 
 
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+## Downloading the model
 
-...
+After installing Sample-Factory, download the model with:
 ```
+python -m sample_factory.huggingface.load_from_hub -r JiajingChen/1
+```
+
+    
+## Using the model
+
+To run the model after download, use the `enjoy` script corresponding to this environment:
+```
+python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=1
+```
+
+
+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 .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=1 --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.
+