Update README.md

README.md

@@ -5,7 +5,7 @@ tags:
 - reinforcement-learning
 - sample-factory
 model-index:
-- name: ATD3
+- name: APPO
   results:
   - task:
       type: reinforcement-learning
@@ -20,7 +20,7 @@ model-index:
       verified: false
 ---
 
-A(n) **ATD3** model trained on the **mujoco_swimmer** environment.
+A(n) **APPO** model trained on the **mujoco_swimmer** 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/
@@ -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 MattStammers/atd3-mujoco-swimmer
+python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-swimmer
 ```
 
     
@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r MattStammers/atd3-mujoco-s
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m sf_examples.mujoco.enjoy_mujoco --algo=ATD3 --env=mujoco_swimmer --train_dir=./train_dir --experiment=atd3-mujoco-swimmer
+python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=appo-mujoco-swimmer
 ```
 
 
@@ -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 sf_examples.mujoco.train_mujoco --algo=ATD3 --env=mujoco_swimmer --train_dir=./train_dir --experiment=atd3-mujoco-swimmer --restart_behavior=resume --train_for_env_steps=10000000000
+python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=appo-mujoco-swimmer --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.