Model Terminal
lagessiehcs/ppo-LunarLander-v2
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. Source: supabase.
Value score
Not scored
Context
—
tokens
Max output
—
tokens
Price
—
Capability radar
Peer value bars
Identity
- Developer
- Safe Superintelligence
- Openness
- —
- Modalities
- —
- Release
- —
- Knowledge cutoff
- —
- Deprecation
- —
- API docs
- —
Benchmarks
No benchmark scores yet.
Compare nearby
Pricing
- Input / 1M
- —
- Output / 1M
- —
- Speed
- —