Add certification model metadata

README.md

@@ -1,35 +1,29 @@
 ---
-library_name: ml-agents
 tags:
-- SnowballTarget
-- deep-reinforcement-learning
 - reinforcement-learning
-- ML-Agents-SnowballTarget
----
-
-  # **ppo** Agent playing **SnowballTarget**
-  This is a trained model of a **ppo** agent playing **SnowballTarget**
-  using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
-
-  ## Usage (with ML-Agents)
-  The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
+- ml-agents
+- ML-Agents-Pyramids
+- deep-reinforcement-learning
+- unity-ml-agents
 
-  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
-  - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
-  browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
-  - A *longer tutorial* to understand how works ML-Agents:
-  https://huggingface.co/learn/deep-rl-course/unit5/introduction
+model-index:
+- name: Pyramids
+  results:
+  - task:
+      type: reinforcement-learning
+      name: ML-Agents-Pyramids
+    dataset:
+      name: ML-Agents-Pyramids
+      type: reinforcement-learning
+    metrics:
+    - type: mean_reward
+      value: "0"
+      name: mean_reward
+---
 
-  ### Resume the training
-  ```bash
-  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
-  ```
+# Pyramids
 
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser**
+Trained Pyramids agent using Unity ML-Agents.
 
-  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
-  2. Step 1: Find your model_id: RBhumi/ppo-Pyramids
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
-  
+Environment: ML-Agents-Pyramids
+Library: ML-Agents