ppo-LunarLander-v2

25
1
license:mit
by
Adilbai
Other
OTHER
New
25 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by ppo-LunarLander-v2 with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagepython
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

# Load the model from Hugging Face Hub
model = load_from_hub(
    repo_id="Adilbai/ppo-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip"
)

# Create environment
env = gym.make("LunarLander-v2", render_mode="human")

# Run the trained agent
obs, info = env.reset()
for _ in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()
env.close()
Usagepython
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

# Load the model from Hugging Face Hub
model = load_from_hub(
    repo_id="Adilbai/ppo-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip"
)

# Create environment
env = gym.make("LunarLander-v2", render_mode="human")

# Run the trained agent
obs, info = env.reset()
for _ in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()
env.close()

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.