# CropCoach
Developed by Noureddine Ech-chouky, AgriEdge(c) 2022
## Initialing CropCoach with default parameters:
```python
import gym
# -- Initializing the environment : with the default parameters
env = gym.make("CropCoach-v0")
```
## Examples of How To Use (Alpha Version)
Initiation the environment
```python
import gym
# -- Initializing the environment : with the default parameters
env = gym.make("CropCoach-v0")
# -- Sample from observation space :
print(env.observation_space.sample())
# -- Sample from action space :
print(env.action_space.sample())
```
Simple Rl loop
```python
import gym
import CropCoach
import shutup
shutup.please()
# -- Define the env :
env = gym.make("CropCoach-v0")
# -- Test the env : the rewarding mecanism
episodes = 10
for episode in range(1, episodes + 1):
state = env.reset()
done = False
score = 0
while not done:
# env.render()
action = env.action_space.sample()
# action = np.array([1000])
print(f"----------- action : {action} -------------\n")
n_state, reward, done, info = env.step(action)
print(f"----------- done : {done} -------------\n")
print(f"----------- state : {n_state} -------------\n")
print(f"----------- reward : {reward} -------------\n")
score += reward
print(f"Episode --:{episode} Score --:{score}")
```
Train agents Using stable baselines3 : you need to install stable baselines3 and configure wandb platform, see : [my wofost project](https://github.com/nour3467/Rl-wofost-Agriculture-Recomender)
```python
# -- Importing dependencies :
import gym
from CropCoach
import numpy as np
import random
import os
import time
from pprint import pprint
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3 import A2C, PPO
def create_list_dir(paths_list):
for path in paths_list:
if not os.path.exists(path):
os.makedirs(path)
# logs folder :
from datetime import datetime
# datetime object containing current date and time
now = datetime.now().strftime("%a_%b_%Y_%I_%M_%p")
Test_Objective = "Default_PPO_vs_A2C_MultiDiscrete"
logs_dir = f"logs_{Test_Objective}_{now}"
models_dir = f"models_{Test_Objective}_{now}"
paths_list = [logs_dir, models_dir]
# create the logs and models folders :
create_list_dir(paths_list)
# # -- Init the env :
env = gym.make("wofost-v0")
# -- Use weights and biases track training and evaluation :
import wandb
from wandb.integration.sb3 import WandbCallback
# -- Init wandb configuration :
config = {"policy_type": "MlpPolicy", "total_timesteps": 10000}
experiment_name = f"{Test_Objective}_{now}"
run = wandb.init(
name=experiment_name,
project="Paper_Experiments",
config=config,
sync_tensorboard=True, # auto-upload sb3's tensorboard metrics
save_code=True, # optional
)
TIMESTEPS = config["total_timesteps"]
a2c_agent = A2C("MlpPolicy", env, tensorboard_log=logs_dir, verbose=1)
ppo_agent = PPO("MlpPolicy", env, tensorboard_log=logs_dir, verbose=1)
print("Training the A2C agent...")
a2c_agent.learn(
total_timesteps=TIMESTEPS,
tb_log_name="A2C",
callback=WandbCallback(
gradient_save_freq=2,
log="all",
verbose=1,
),
)
a2c_agent.save(f"{models_dir}/a2c_{TIMESTEPS}")
print("Done training the A2C agent")
print("Training the PPO agent...")
ppo_agent.learn(
total_timesteps=TIMESTEPS,
tb_log_name="PPO",
callback=WandbCallback(
gradient_save_freq=2,
log="all",
verbose=1,
),
)
ppo_agent.save(f"{models_dir}/ppo_{TIMESTEPS}")
print("Done training the PPO agent")
run.finish()
env.close()
```
Changing the default parameters :
```python
import gym
import crop_coach
import shutup
shutup.please()
crop_path = "C:/Users/noureddine/Desktop/default_data/crop/crop.cab"
site_path = "C:/Users/noureddine/Desktop/default_data/site/site.cab"
soil_path = "C:/Users/noureddine/Desktop/default_data/soil/soil.cab"
args={
"files_paths": {
"site":site_path, "soil":soil_path, "crop":crop_path
},
"sample_year":True,
"Agromanager_dict":{
"crop_name": "wheat","crop_variety": "Winter_wheat_101","campaign_start_date": "-01-01","crop_start_type":"emergence","emergence_date": "-03-31","crop_end_type": "harvest","harvest_date": "-08-11", "max_duration": 300
},
"Costs_dict": {"Irrigation": 150, "N": 8, "P": 8.5, "K": 7, "Selling": 2.5},
"Discount_factors_dict": {"Irrigation": 1, "N": 1, "P": 1, "K": 1},
"year": 2019,
"years_count": 1,
}
# -- Define the env :
env = gym.make("CropCoach-v0", **args)
# -- Test the env : the rewarding mecanism
episodes = 10
for episode in range(1, episodes + 1):
state = env.reset()
done = False
score = 0
while not done:
env.render()
action = env.action_space.sample()
# action = np.array([1000])
print(f"----------- action : {action} -------------\n")
n_state, reward, done, info = env.step(action)
print(f"----------- done : {done} -------------\n")
print(f"----------- state : {n_state} -------------\n")
print(f"----------- reward : {reward} -------------\n")
score += reward
print(f"Episode --:{episode} Score --:{score}")
```
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"description": "\n# CropCoach\n\n\n\nDeveloped by Noureddine Ech-chouky, AgriEdge(c) 2022\n\n\n\n## Initialing CropCoach with default parameters:\n\n\n\n```python\n\nimport gym\n\n\n\n# -- Initializing the environment : with the default parameters\n\nenv = gym.make(\"CropCoach-v0\")\n\n```\n\n\n\n## Examples of How To Use (Alpha Version)\n\n\n\nInitiation the environment\n\n\n\n```python\n\n\n\nimport gym\n\n\n\n# -- Initializing the environment : with the default parameters\n\nenv = gym.make(\"CropCoach-v0\")\n\n\n\n# -- Sample from observation space :\n\nprint(env.observation_space.sample())\n\n\n\n# -- Sample from action space :\n\nprint(env.action_space.sample())\n\n\n\n```\n\n\n\nSimple Rl loop\n\n\n\n```python\n\n\n\nimport gym\n\nimport CropCoach\n\n\n\nimport shutup\n\n\n\nshutup.please()\n\n\n\n\n\n# -- Define the env :\n\nenv = gym.make(\"CropCoach-v0\")\n\n\n\n\n\n\n\n# -- Test the env : the rewarding mecanism\n\nepisodes = 10\n\nfor episode in range(1, episodes + 1):\n\n state = env.reset()\n\n done = False\n\n score = 0\n\n\n\n while not done:\n\n # env.render()\n\n action = env.action_space.sample()\n\n # action = np.array([1000])\n\n print(f\"----------- action : {action} -------------\\n\")\n\n\n\n n_state, reward, done, info = env.step(action)\n\n print(f\"----------- done : {done} -------------\\n\")\n\n print(f\"----------- state : {n_state} -------------\\n\")\n\n print(f\"----------- reward : {reward} -------------\\n\")\n\n score += reward\n\n\n\n print(f\"Episode --:{episode} Score --:{score}\")\n\n```\n\n\n\nTrain agents Using stable baselines3 : you need to install stable baselines3 and configure wandb platform, see : [my wofost project](https://github.com/nour3467/Rl-wofost-Agriculture-Recomender)\n\n\n\n```python\n\n\n\n# -- Importing dependencies :\n\nimport gym\n\nfrom CropCoach\n\nimport numpy as np\n\nimport random\n\nimport os\n\nimport time\n\nfrom pprint import pprint\n\nfrom stable_baselines3.common.callbacks import BaseCallback\n\nfrom stable_baselines3 import A2C, PPO\n\n\n\n\n\ndef create_list_dir(paths_list):\n\n for path in paths_list:\n\n if not os.path.exists(path):\n\n os.makedirs(path)\n\n\n\n\n\n# logs folder :\n\nfrom datetime import datetime\n\n\n\n# datetime object containing current date and time\n\nnow = datetime.now().strftime(\"%a_%b_%Y_%I_%M_%p\")\n\n\n\n\n\nTest_Objective = \"Default_PPO_vs_A2C_MultiDiscrete\"\n\n\n\nlogs_dir = f\"logs_{Test_Objective}_{now}\"\n\nmodels_dir = f\"models_{Test_Objective}_{now}\"\n\npaths_list = [logs_dir, models_dir]\n\n\n\n# create the logs and models folders :\n\ncreate_list_dir(paths_list)\n\n\n\n\n\n# # -- Init the env :\n\nenv = gym.make(\"wofost-v0\")\n\n\n\n# -- Use weights and biases track training and evaluation :\n\nimport wandb\n\nfrom wandb.integration.sb3 import WandbCallback\n\n\n\n\n\n# -- Init wandb configuration :\n\nconfig = {\"policy_type\": \"MlpPolicy\", \"total_timesteps\": 10000}\n\n\n\nexperiment_name = f\"{Test_Objective}_{now}\"\n\n\n\n\n\nrun = wandb.init(\n\n name=experiment_name,\n\n project=\"Paper_Experiments\",\n\n config=config,\n\n sync_tensorboard=True, # auto-upload sb3's tensorboard metrics\n\n save_code=True, # optional\n\n)\n\n\n\n\n\nTIMESTEPS = config[\"total_timesteps\"]\n\n\n\na2c_agent = A2C(\"MlpPolicy\", env, tensorboard_log=logs_dir, verbose=1)\n\n\n\nppo_agent = PPO(\"MlpPolicy\", env, tensorboard_log=logs_dir, verbose=1)\n\n\n\n\n\nprint(\"Training the A2C agent...\")\n\na2c_agent.learn(\n\n total_timesteps=TIMESTEPS,\n\n tb_log_name=\"A2C\",\n\n callback=WandbCallback(\n\n gradient_save_freq=2,\n\n log=\"all\",\n\n verbose=1,\n\n ),\n\n)\n\na2c_agent.save(f\"{models_dir}/a2c_{TIMESTEPS}\")\n\nprint(\"Done training the A2C agent\")\n\n\n\nprint(\"Training the PPO agent...\")\n\nppo_agent.learn(\n\n total_timesteps=TIMESTEPS,\n\n tb_log_name=\"PPO\",\n\n callback=WandbCallback(\n\n gradient_save_freq=2,\n\n log=\"all\",\n\n verbose=1,\n\n ),\n\n)\n\nppo_agent.save(f\"{models_dir}/ppo_{TIMESTEPS}\")\n\nprint(\"Done training the PPO agent\")\n\n\n\nrun.finish()\n\n\n\nenv.close()\n\n```\n\n\n\nChanging the default parameters :\n\n\n\n```python\n\n\n\nimport gym\n\nimport crop_coach\n\n\n\nimport shutup\n\n\n\nshutup.please()\n\n\n\n\n\n\n\n\n\n\n\ncrop_path = \"C:/Users/noureddine/Desktop/default_data/crop/crop.cab\"\n\nsite_path = \"C:/Users/noureddine/Desktop/default_data/site/site.cab\"\n\nsoil_path = \"C:/Users/noureddine/Desktop/default_data/soil/soil.cab\"\n\n\n\n\n\n\n\nargs={\n\n \"files_paths\": {\n\n \"site\":site_path, \"soil\":soil_path, \"crop\":crop_path\n\n },\n\n \"sample_year\":True,\n\n \"Agromanager_dict\":{\n\n \"crop_name\": \"wheat\",\"crop_variety\": \"Winter_wheat_101\",\"campaign_start_date\": \"-01-01\",\"crop_start_type\":\"emergence\",\"emergence_date\": \"-03-31\",\"crop_end_type\": \"harvest\",\"harvest_date\": \"-08-11\", \"max_duration\": 300\n\n },\n\n \"Costs_dict\": {\"Irrigation\": 150, \"N\": 8, \"P\": 8.5, \"K\": 7, \"Selling\": 2.5},\n\n \"Discount_factors_dict\": {\"Irrigation\": 1, \"N\": 1, \"P\": 1, \"K\": 1},\n\n \"year\": 2019,\n\n \"years_count\": 1,\n\n }\n\n\n\n# -- Define the env :\n\nenv = gym.make(\"CropCoach-v0\", **args)\n\n\n\n# -- Test the env : the rewarding mecanism\n\nepisodes = 10\n\nfor episode in range(1, episodes + 1):\n\n state = env.reset()\n\n done = False\n\n score = 0\n\n\n\n while not done:\n\n env.render()\n\n action = env.action_space.sample()\n\n # action = np.array([1000])\n\n print(f\"----------- action : {action} -------------\\n\")\n\n\n\n n_state, reward, done, info = env.step(action)\n\n print(f\"----------- done : {done} -------------\\n\")\n\n print(f\"----------- state : {n_state} -------------\\n\")\n\n print(f\"----------- reward : {reward} -------------\\n\")\n\n score += reward\n\n\n\n print(f\"Episode --:{episode} Score --:{score}\")\n\n```\n",
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