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ppo-LunarLander-v2
MorganWKen/ppo-LunarLander-v2
ppo-LunarLander-v2 is a reinforcement learning model from MorganWKen. 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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.mp4165 KB · 35%
At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- May 4, 2024
- Updated
- May 5, 2024
- SHA
- 295498fa
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)
model = PPO('MlpPolicy', 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-LunarLander-v2"
model.save(model_name)
eval_env = Monitor(gym.make("LunarLander-v2"))
# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# mean_reward=266.997 +/- 21.582
Type
Base model given from Hugging Face.
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- May 4, 2024
- Updated
- May 5, 2024