metadata
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 242.40 +/- 15.91
name: mean_reward
verified: false
PPO Agent playing LunarLander-v2
This model is trained using PPO [proximal policy optimization algorithm invented by OpenAI] The RL-based agent playing to land correctly on the moon using LunarLander environment as simulator.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
repo_id = "innocent-charles/RL-ppo-LunarLander-v2"
filename = "RL-ppo-LunarLander-v2.zip"
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
...