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ppo-PyramidsTraining

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by KraTUZen reinforcement-learning ml-agents
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Model card

🏛️ PPO Agent on Pyramids

This repository contains a trained Proximal Policy Optimization (PPO) agent that plays the Pyramids environment using the Unity ML-Agents Library.

📊 Model Card

Model Name: ppo-PyramidsTraining
Environment: Pyramids (Unity ML-Agents)
Algorithm: PPO (Proximal Policy Optimization)
Performance Metric:

  • Achieves stable performance in navigating and solving pyramid-based tasks
  • Demonstrates convergence to an effective policy

🚀 Usage (with ML-Agents)

Documentation: ML-Agents Toolkit Docs

Resume Training

mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume

Load and Run

# Example: loading the trained PPO model
# (requires Unity ML-Agents setup)
model_id = "KraTUZen/ppo-PyramidsTraining"
# Select your .nn or .onnx file from the repo

🧠 Notes

  • The agent is trained using PPO, a robust on-policy algorithm widely used in Unity ML-Agents.
  • The environment involves pyramid navigation and puzzle-solving, requiring precision and strategy.
  • The trained model is stored as .nn or .onnx files for direct Unity integration.

📂 Repository Structure

  • Pyramids.nn / Pyramids.onnx → Trained PPO policy
  • README.md → Documentation and usage guide

✅ Results

  • The agent learns to navigate pyramid structures and solve tasks efficiently.
  • Demonstrates stable training and effective policy convergence using PPO.

🔎 Environment Overview

  • Observation Space: Continuous (agent position, pyramid state, environment features)
  • Action Space: Continuous (movement, interaction)
  • Objective: Solve pyramid-based tasks and maximize rewards
  • Reward: Positive reward for successful task completion, penalties for failures

📚 Learning Highlights

  • Algorithm: PPO (Proximal Policy Optimization)
  • Update Rule: Clipped surrogate objective to ensure stable updates
  • Strengths: Robust, stable, widely used in Unity ML-Agents
  • Limitations: Requires careful tuning of hyperparameters (clip ratio, learning rate, batch size)

🎮 Watch Your Agent Play

You can watch your agent directly in your browser:

  1. Visit Unity ML-Agents on Hugging Face
  2. Find your model ID: KraTUZen/ppo-PyramidsTraining
  3. Select your .nn or .onnx file
  4. Click Watch the agent play 👀