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README.md

@@ -1,35 +1,27 @@
 ---
-library_name: ml-agents
 tags:
-- Pyramids
-- deep-reinforcement-learning
+- Pixelcopter-PLE-v0
+- reinforce
 - reinforcement-learning
-- ML-Agents-Pyramids
+- custom-implementation
+- deep-rl-class
+model-index:
+- name: '9'
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Pixelcopter-PLE-v0
+      type: Pixelcopter-PLE-v0
+    metrics:
+    - type: mean_reward
+      value: 44.30 +/- 41.50
+      name: mean_reward
+      verified: false
 ---
 
-  # **ppo** Agent playing **Pyramids**
-  This is a trained model of a **ppo** agent playing **Pyramids**
-  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/
-
-  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
-
-  ### Resume the training
-  ```bash
-  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
-  ```
-
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser**
-
-  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
-  2. Step 1: Find your model_id: JiajingChen/9
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
+  # **Reinforce** Agent playing **Pixelcopter-PLE-v0**
+  This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
+  To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
   

hyperparameters.json

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+{"h_size": 64, "n_training_episodes": 50000, "n_evaluation_episodes": 10, "max_t": 10000, "gamma": 0.99, "lr": 0.0001, "env_id": "Pixelcopter-PLE-v0", "state_space": 7, "action_space": 2}

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6390d496559de0d1b79b5aa54ae740468974399d119913e299f54d228962948b
+size 39668

results.json

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+{"env_id": "Pixelcopter-PLE-v0", "mean_reward": 44.3, "n_evaluation_episodes": 10, "eval_datetime": "2024-02-16T00:05:05.871217"}