Delete Huggy2

Huggy2/README.md

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----
-library_name: ml-agents
-tags:
-- Huggy
-- deep-reinforcement-learning
-- reinforcement-learning
-- ML-Agents-Huggy
----
-
-  # **ppo** Agent playing **Huggy**
-  This is a trained model of a **ppo** agent playing **Huggy**
-  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: wefio/ppo-Huggy
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
-  

Huggy2/config.json

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-{"behaviors": {"Huggy": {"trainer_type": "ppo", "threaded": true, "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0003, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 5, "learning_rate_schedule": "linear"}, "curiosity": {"strength": 0.01, "gamma": 0.995}, "network_settings": {"normalize": true, "hidden_units": 512, "num_layers": 3, "vis_encode_type": "simple"}, "reward_signals": {"extrinsic": {"gamma": 0.995, "strength": 1.0}}, "checkpoint_interval": 200000, "keep_checkpoints": 15, "max_steps": "2e6", "time_horizon": 1000, "summary_freq": 50000}}}

Huggy2/configuration.yaml

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-behaviors:
-  Huggy:
-    trainer_type: ppo
-    threaded: True
-    hyperparameters:
-      batch_size: 2048
-      buffer_size: 20480
-      learning_rate: 0.0003
-      beta: 0.005
-      epsilon: 0.2
-      lambd: 0.95
-      num_epoch: 5
-      learning_rate_schedule: linear
-    curiosity:
-      strength: 0.01
-      gamma: 0.995
-    network_settings:
-      normalize: true
-      hidden_units: 512
-      num_layers: 3
-      vis_encode_type: simple
-    reward_signals:
-      extrinsic:
-        gamma: 0.995
-        strength: 1.0
-    checkpoint_interval: 200000
-    keep_checkpoints: 15
-    max_steps: 2e6
-    time_horizon: 1000
-    summary_freq: 50000