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ppo-LunarLander-v3
suveda999/ppo-LunarLander-v3
ppo-LunarLander-v3 is a reinforcement learning model from suveda999. 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 stable-baselines3.
This is a Proximal Policy Optimization (PPO) agent trained using Stable-Baselines3 on the LunarLander environment.
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.zip150 KB · 47%
At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Sep 18, 2026
- Updated
- Sep 18, 2026
- SHA
- 7352034b
Model card
From the Hugging Face model README
PPO LunarLander-v3
This is a Proximal Policy Optimization (PPO) agent trained using Stable-Baselines3 on the LunarLander environment.
Model Details
- Algorithm: PPO (Proximal Policy Optimization)
- Environment: LunarLander-v3
- Library: Stable-Baselines3
- Framework: PyTorch
- Task: Deep Reinforcement Learning
Evaluation Results
The trained agent was evaluated over multiple episodes.
- Mean reward: 271.62
- Standard deviation: 12.83
- Leaderboard score: 258.79
The leaderboard score is calculated as:
mean_reward - std_reward
Therefore:
271.62 - 12.83 = 258.79
Training
The model was trained using the Proximal Policy Optimization (PPO) algorithm from Stable-Baselines3.
The trained model is provided as:
ppo-LunarLander-v3.zip
Usage
You can load the trained model using Stable-Baselines3:
from stable_baselines3 import PPO
model = PPO.load("ppo-LunarLander-v3")
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Sep 18, 2026
- Updated
- Sep 18, 2026