Metaseeker348/ppo-actor-critic

🧠 PPO-CartPole Agent

This is a PPO (Proximal Policy Optimization) agent trained to solve the CartPole-v1 environment using PyTorch.

🛠️ Model Details

• Algorithm: PPO (Proximal Policy Optimization)
• Environment: CartPole-v1
• Framework: PyTorch
• Observation Space: Continuous (4-dim)
• Action Space: Discrete (2 actions)
• Training Episodes: 1000
• Max Steps per Episode: 500

🚀 Usage

You can load the model using PyTorch:

import torch
from your_model_file import PolicyNetwork  # replace with your actual class name

model = PolicyNetwork()
model.load_state_dict(torch.load("ppo_cartpole.pt"))
model.eval()


# PPO CartPole Agent 🏋️

This repository contains a PPO agent trained to solve the CartPole-v1 environment using PyTorch and Gymnasium.

## 🎥 Episode Demo

<video controls width="600">
  <source src="ppo-episode-0.mp4" type="video/mp4">
  Your browser does not support the video tag.
</video>