PPO LunarLander-v2
A Proximal Policy Optimization (PPO) agent trained using Stable-Baselines3 on the Gymnasium LunarLander-v2 environment.
Environment
- Environment: LunarLander-v2
- Algorithm: PPO
- Policy: MlpPolicy
Training Details
- Total Training Timesteps: 1,000,000
- Framework: Stable-Baselines3
- Library: Gymnasium
Evaluation Results
Evaluated over 10 episodes:
| Metric | Result |
|---|---|
| Mean Reward | 246.68 |
| Standard Deviation | 19.05 |
| Mean Reward - Std | 227.63 |
Model File
ppo-LunarLander-v2.zip
Evaluation Video
Technologies
- Python
- Stable-Baselines3
- Gymnasium
- Proximal Policy Optimization
- Reinforcement Learning
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Evaluation results
- Mean Reward on LunarLander-v2self-reported246.680
- Standard Deviation on LunarLander-v2self-reported19.050