---
license: mit
tags:
  - reinforcement-learning
  - deep-rl
  - ppo
  - cartpole
library_name: torch
framework: pytorch
model-index:
  - name: PPO-CartPole
    results: []
---

# 🧠 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:

```python
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>