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ppo-LunarLander-v3
kapoorkrish/ppo-LunarLander-v3
ppo-LunarLander-v3 is a reinforcement learning model from kapoorkrish. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This is a trained PPO agent playing LunarLander-v3, using a custom single-file PyTorch implementation (closely following the CleanRL PPO reference).
Access
Public
Updated Jul 19, 2026
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.mp4158 KB · 77%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Jul 19, 2026
- Updated
- Jul 19, 2026
- SHA
- 1a2360f4
Model card
From the Hugging Face model README
PPO Agent Playing LunarLander-v3
This is a trained PPO agent playing LunarLander-v3, using a custom single-file PyTorch implementation
(closely following the CleanRL PPO reference).
Evaluation
Mean reward over 10 episodes: 12.81 +/- 87.50
Hyperparameters
num_envs = 4
num_steps = 128
learning_rate = 0.00025
total_timesteps = 500000
gamma = 0.99
gae_lambda = 0.95
epochs = 4
clip_coef = 0.2
entropy_coef = 0.01
vf_coef = 0.5
max_grad_norm = 0.5
batch_size = 512
minibatch_size = 128
Usage
import torch
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="kapoorkrish/ppo-LunarLander-v3", filename="model.pt")
agent = Agent(envs) # rebuild the same Agent class/architecture used in training
agent.load_state_dict(torch.load(model_path))
agent.eval()
- Task
- Reinforcement Learning
- Created
- Jul 19, 2026
- Updated
- Jul 19, 2026