Huggy

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