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RL-Unit1
Muddassir/RL-Unit1
RL-Unit1 is a reinforcement learning model from Muddassir. 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 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
- Jul 25, 2023
- Updated
- Jul 25, 2023
- SHA
- 5e718db4
Model card
From the Hugging Face model README
** ** Agent playing LunarLander-v2
This is a trained model of a ** ** agent playing LunarLander-v2 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
model_name = "ppo-LunarLander-v2-Muddassir"
model.save(model_name)
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}")
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}")
...
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Jul 25, 2023
- Updated
- Jul 25, 2023