Add trained model and demo video

.gitattributes

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 *tfevents* filter=lfs diff=lfs merge=lfs -text
 3DBall_Demo.mp4 filter=lfs diff=lfs merge=lfs -text
 3DBall_Demo[[:space:]](online-video-cutter.com).mp4 filter=lfs diff=lfs merge=lfs -text
+3DBall_Demo_(1).mp4 filter=lfs diff=lfs merge=lfs -text

3DBall_Demo_(1).mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:58a928222520572fa05bd7b2c8315ea4f481f36a5432b4cbf5fe98b7491a61fa
+size 1611615

README.md

@@ -1,54 +1,35 @@
----
-library_name: ml-agents
-tags:
-  - 3d-ball
-  - deep-reinforcement-learning
-  - reinforcement-learning
-  - ppo
-  - unity-ml-agents
----
-
-# 3DBall Trained Agent
-
-This is a trained model of a PPO agent playing the 3DBall environment, created using the Unity ML-Agents library. The agent learns to balance a ball on a moving platform for as long as possible.
-
-### Training Hyperparameters
-
-The agent was trained using the following configuration from the `3DBall.yaml` file:
-
-```yaml
-behaviors:
-  3DBall:
-    trainer_type: ppo
-    hyperparameters:
-      learning_rate: 0.0003
-      learning_rate_schedule: linear
-      beta: 0.0005
-      epsilon: 0.2
-      lambd: 0.95
-      num_epoch: 3
-      buffer_size: 2048
-      batch_size: 256
-      time_horizon: 1024
-    network_settings:
-      normalize: false
-      hidden_units: 128
-      num_layers: 2
-      vis_encode_type: simple
-    reward_signals:
-      extrinsic:
-        gamma: 0.99
-        strength: 1.0
-    checkpoint_interval: 500000
-    threaded: true
-```
-
-### Video Demo
-
-Here is a video of the trained agent in action, demonstrating the learned behavior.
-
-<video controls width="100%">
-  <source src="[results/3DBallTraining/`3DBall_Demo (online-video-cutter.com).mp4`]("C:/Users/KUNAL/OneDrive/Desktop/Pirate_AP/DeepRL/results/3DBallTraining/`3DBall_Demo (online-video-cutter.com).mp4`")" type="video/mp4">
-  Your browser does not support the video tag.
-</video>
-
+---
+library_name: ml-agents
+tags:
+- 3DBall
+- deep-reinforcement-learning
+- reinforcement-learning
+- ML-Agents-3DBall
+---
+
+  # **ppo** Agent playing **3DBall**
+  This is a trained model of a **ppo** agent playing **3DBall**
+  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: VisionaryKunal/3DBall-MLAgents
+  3. Step 2: Select your *.nn /*.onnx file
+  4. Click on Watch the agent play 👀
+