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ppo-LunarLander-v2
Jojo78/ppo-LunarLander-v2
ppo-LunarLander-v2 is a reinforcement learning model from Jojo78. 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 23, 2023
- Updated
- Feb 23, 2023
- SHA
- c9960b35
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)
Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16)
Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
if we had frames as input we would use CnnPolicy
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, )
Training the model for 3,000,000 timesteps
model.learn(total_timesteps=3000000)
Save the model
model_name = "ppo-LunarLander-v2" model.save(model_name)
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Feb 23, 2023
- Updated
- Feb 23, 2023