Update README.md


README.md

@@ -5,31 +5,95 @@ tags:
 - deep-reinforcement-learning
 - reinforcement-learning
 - ML-Agents-Pyramids
+license: apache-2.0
 ---
 
   # **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/
+## Watch the Agent play
+You can watch the agent playing directly in your browser
 
-  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
+Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
+Step 1: Find the model_id: Francesco-A/ppo-Pyramids-v1
+Step 2: Select the .nn /.onnx file
+Click on Watch the agent play
 
   ### 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**
+### Training hyperparameters
+```python
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      rnd:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          hidden_units: 64
+          num_layers: 3
+        learning_rate: 0.0001
+    keep_checkpoints: 5
+    max_steps: 1000000
+    time_horizon: 128
+    summary_freq: 30000
+```
 
-  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
-  2. Step 1: Find your model_id: Francesco-A/ppo-Pyramids-v1
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
-  
+## Training details
+| Step    | Time Elapsed | Mean Reward | Std of Reward | Status    |
+|---------|--------------|-------------|---------------|-----------|
+| 30000   | 59.481 s     | -1.000      | 0.000         | Training  |
+| 60000   | 118.648 s    | -0.798      | 0.661         | Training  |
+| 90000   | 180.684 s    | -0.701      | 0.808         | Training  |
+| 120000  | 240.734 s    | -0.931      | 0.373         | Training  |
+| 150000  | 300.978 s    | -0.851      | 0.588         | Training  |
+| 180000  | 360.137 s    | -0.934      | 0.361         | Training  |
+| 210000  | 424.326 s    | -1.000      | 0.000         | Training  |
+| 240000  | 484.774 s    | -0.849      | 0.595         | Training  |
+| 270000  | 546.089 s    | -0.377      | 1.029         | Training  |
+| 300000  | 614.797 s    | -0.735      | 0.689         | Training  |
+| 330000  | 684.241 s    | -0.926      | 0.405         | Training  |
+| 360000  | 745.790 s    | -0.819      | 0.676         | Training  |
+| 390000  | 812.573 s    | -0.715      | 0.755         | Training  |
+| 420000  | 877.836 s    | -0.781      | 0.683         | Training  |
+| 450000  | 944.423 s    | -0.220      | 1.114         | Training  |
+| 480000  | 1010.918 s   | -0.484      | 0.962         | Training  |
+| 510000  | 1074.058 s   | -0.003      | 1.162         | Training  |
+| 540000  | 1138.848 s   | -0.021      | 1.222         | Training  |
+| 570000  | 1204.326 s   | 0.384       | 1.231         | Training  |
+| 600000  | 1276.488 s   | 0.690       | 1.174         | Training  |
+| 630000  | 1345.297 s   | 0.943       | 1.058         | Training  |
+| 660000  | 1412.791 s   | 1.014       | 1.043         | Training  |
+| 690000  | 1482.712 s   | 0.927       | 1.054         | Training  |
+| 720000  | 1548.726 s   | 0.900       | 1.128         | Training  |
+| 750000  | 1618.284 s   | 1.379       | 0.701         | Training  |
+| 780000  | 1692.080 s   | 1.567       | 0.359         | Training  |
+| 810000  | 1762.159 s   | 1.475       | 0.567         | Training  |
+| 840000  | 1832.166 s   | 1.438       | 0.648         | Training  |
+| 870000  | 1907.191 s   | 1.534       | 0.536         | Training  |
+| 900000  | 1977.521 s   | 1.552       | 0.478         | Training  |
+| 930000  | 2051.259 s   | 1.458       | 0.633         | Training  |
+| 960000  | 2126.498 s   | 1.545       | 0.586         | Training  |
+| 990000  | 2198.591 s   | 1.565       | 0.591         | Training  |