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ppo-actor-critic
Metaseeker348/ppo-actor-critic
ppo-actor-critic is a reinforcement learning model from Metaseeker348. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for torch. The card lists the license as mit.
This is a PPO (Proximal Policy Optimization) agent trained to solve the CartPole-v1 environment using PyTorch.
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Public
Updated Jul 17, 2025
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At a glance
- Task
- Reinforcement Learning
- Library
- torch
- License
- mit
- Access
- Public
- Created
- Jul 17, 2025
- Updated
- Jul 17, 2025
- SHA
- 3ef0db35
Model card
From the Hugging Face model README
🧠 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>
- Task
- Reinforcement Learning
- Library
- torch
- License
- mit
- Created
- Jul 17, 2025
- Updated
- Jul 17, 2025