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Pyramids-1000068.onnx

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Pyramids-1000068.pt

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Pyramids-499974.pt

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Pyramids-999940.onnx

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README.md

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+---
+library_name: ml-agents
+tags:
+- PyramidsRND
+- deep-reinforcement-learning
+- reinforcement-learning
+- ML-Agents-PyramidsRND
+---
+
+  # **ppo** Agent playing **PyramidsRND**
+  This is a trained model of a **ppo** agent playing **PyramidsRND**
+  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: AE1999/RND-Pyramids
+  3. Step 2: Select your *.nn /*.onnx file
+  4. Click on Watch the agent play 👀
+  

checkpoint.pt

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config.json

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+{"behaviors": {"PyramidsRND": {"batch_size": 128, "buffer_size": 4096, "gamma": 0.99, "hidden_units": 256, "learning_rate": 0.0003, "max_steps": 1000000, "num_layers": 2, "trainer_type": "ppo"}}}

configuration.yaml

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+behaviors:
+  PyramidsRND:
+    batch_size: 128
+    buffer_size: 4096
+    gamma: 0.99
+    hidden_units: 256
+    learning_rate: 0.0003
+    max_steps: 1000000
+    num_layers: 2
+    trainer_type: ppo

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