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ppo-LunarLander-v3
DevilNReality/ppo-LunarLander-v3
ppo-LunarLander-v3 is a reinforcement learning model from DevilNReality. 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 project trains a reinforcement learning agent using Proximal Policy Optimization (PPO) to solve the LunarLander-v3 environment from Gymnasium. The agent learns to land a lunar module safely between two flags usin…
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Nov 12, 2025
- Updated
- Nov 12, 2025
- SHA
- 4ed7e7e9
Model card
From the Hugging Face model README
🚀 PPO Agent for LunarLander-v3
This project trains a reinforcement learning agent using Proximal Policy Optimization (PPO) to solve the LunarLander-v3 environment from Gymnasium. The agent learns to land a lunar module safely between two flags using Box2D physics.
🧠 Project Summary
- Environment: LunarLander-v3 (Box2D)
- Algorithm: PPO (Stable-Baselines3)
- Training: 1 million timesteps using vectorized environments
- Evaluation: Mean reward ~269 ± 11.6 over 10 episodes
- Replay: Manually recorded and uploaded to Hugging Face
📁 Hugging Face Model Card
The trained model is available at:
🔗 DevilNReality/ppo-LunarLander-v3
Includes:
- Model weights
- Evaluation metrics
- Replay video
- Auto-generated metadata
🎬 Replay Video
Watch the agent land in the environment:
✅ Certification
This model satisfies the Hugging Face Deep RL course Unit 1 requirements:
- Trained PPO agent on
LunarLander-v3 - Evaluation score ≥ 200
- Model pushed to Hugging Face
- Replay video included
📚 References
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Languages
- en
- Created
- Nov 12, 2025
- Updated
- Nov 12, 2025