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kuds/
rl-lunar-lander-continuous-sac

Name: rl-lunar-lander-continuous-sac
Availability: InStock

stable-baselines3

reinforcement-learning

LunarLander-v3

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SAC Agent playing LunarLanderContinuous-v3

Then, you can load the model using the following Python code:

python

import gymnasium as gym
from stable_baselines3 import SAC
from stable_baselines3.common.env_util import make_vec_env

# Load the trained model
model = SAC.load("best-model.zip")

# Create the environment
env = make_vec_env("LunarLanderContinuous-v3", n_envs=1)

# Reset the environment
obs, info = env.reset()

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

Hugging Face Hub

You can also use the Hugging Face Hub to load the model. First, you need to install the Hugging Face Hub library:

pip install huggingface_hub

Then, you can load the model from the hub using the following code:

python

from huggingface_hub import hf_hub_download
import torch as th
import gymnasium as gym
from stable_baselines3 import SAC

# Download the model from the Hub
model_path = hf_hub_download(repo_id="kuds/lunar-lander-sac", filename="best-model.zip")

# Load the model
model = SAC.load(model_path)

# Create the environment
env = make_vec_env("LunarLanderContinuous-v3", n_envs=1)

# Enjoy the trained agent
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, dones, info = env.step(action)
    env.render("human")

kuds/rl-lunar-lander-continuous-sac

SAC Agent playing LunarLanderContinuous-v3

Hugging Face Hub

kuds/
rl-lunar-lander-continuous-sac