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ppo-lunarLander-v2
nadamakram/ppo-lunarLander-v2
ppo-lunarLander-v2 is a reinforcement learning model from nadamakram. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Feb 22, 2023
- Updated
- Feb 25, 2023
- SHA
- 5a9740e0
Model card
From the Hugging Face model README
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)
TODO: Add your code
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import load_from_hub
env = make_vec_env('LunarLander-v2', n_envs=16)
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 4,
gamma = 0.999,
gae_lambda = 0.98,
ent_coef = 0.01,
verbose=1)
model.learn(total_timesteps=1000000)
model_name = "ppo_lander"
model.save(model_name)
eval_env = 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}")
...
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Feb 22, 2023
- Updated
- Feb 25, 2023