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JohnnyBoy00/
ppo-LunarLander-v2

Name: ppo-LunarLander-v2
Availability: InStock

stable-baselines3

LunarLander-v2

deep-reinforcement-learning

reinforcement-learning

model-index

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PPO Agent playing LunarLander-v2

This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

python

import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_sb3 import load_from_hub

repo_id = "JohnnyBoy00/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"

# The model was trained with Python 3.8, which uses Pickle Protocol 5.
# However, Python 3.6 and 3.7 use Pickle Protocol 4.
# Thus, in order to ensure compatibility, it is necessary to:
# 1. Install pickle5 (we done it at the beginning of the colab);
# 2. Create a custom empty object, which is passed as a parameter to PPO.load().
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)

eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")