Vishand03/HuggyModel

Huggy - Trained Agent

Author: Vishand03
Model Type: Reinforcement Learning (PPO)
Environment: Custom Huggy Environment (ML-Agents)
Framework: ML-Agents + PyTorch

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Description

This model is a trained Huggy agent using the PPO algorithm.
It learns to navigate and complete tasks in the Huggy environment.

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Training Details

• Trainer: PPO
• Steps: ~800,000 (can be resumed)
• Reward: ~3.9 mean reward at the last checkpoint
• Hyperparameters:
  • Batch size: 4096
  • Buffer size: 40960
  • Learning rate: 0.0001
  • Gamma: 0.995
  • Lambda: 0.95

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Usage

from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.base_env import ActionTuple
import onnxruntime as ort

env = UnityEnvironment(file_name="Huggy.x86_64", no_graphics=True)
# Load model
session = ort.InferenceSession("Huggy-799913.onnx")
# Continue with your inference pipeline...