Model comparison
lagessiehcs/ppo-LunarLander-v2 vs o3
Capability radar, value bars, and side-by-side posture.
lagessiehcs/ppo-LunarLander-v2
Not scored
value score
o3
93
value score
lagessiehcs/ppo-LunarLander-v2 ctx
—
o3 ctx
200K
Capability radar
Value · context · multimodal · openness · speed posture
Value head-to-head
- o393
Attribute tape
| Field | lagessiehcs/ppo-LunarLander-v2 | o3 |
|---|---|---|
| Developer | Safe Superintelligence | OpenAI |
| Context | — | 200K |
| Modalities | Text, Code | |
| Openness | — | Proprietary Model |
| Speed | — | — |
| Price | — | $10/1M in · $40/1M out |
| Value score | — | 93 |
| Summary | A PPO-trained reinforcement learning policy for the LunarLander-v2 benchmark environment, where a neural-network agent learns to control a lunar lander's thrusters for safe landing. Trained with Stable-Baselines3 and hosted as a personal model artifact on the Hugging Face Hub. | OpenAI reasoning model focused on hard problems. |