File size: 2,309 Bytes
9d9a6de 4947a47 c27712b 4947a47 c27712b 4947a47 9d9a6de 51e738b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 285.36 +/- 14.99
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
repo_id = "kinkpunk/Lunar-Landing-Program"
filename = "LunarProgram-PPO.zip"
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,
custom_objects=custom_objects,
print_system_info=True)
env = make_vec_env('LunarLander-v2', n_envs=1)
# Evaluate the model
mean_reward, std_reward = evaluate_policy(model, env,
n_eval_episodes=10,
deterministic=True)
# Print the results
print('mean_reward={:.2f} +/- {:.2f}'.format(mean_reward, std_reward))
```
## Training (with Stable-baselines3)
```python
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
# Create the evaluation envs
env = make_vec_env('LunarLander-v2', n_envs=16)
env = gym.make('LunarLander-v2')
# Instantiate the agent
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 32,
n_epochs = 8,
gamma = 0.99,
gae_lambda = 0.95,
ent_coef = 0.01,
verbose=1,
seed=2022)
# Train
model.learn(total_timesteps=1500000)
# Save model
model_name = "Any-Name"
model.save(model_name)
```
|