metadata
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:
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>