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feat(command): add args parsing for all RL agent #3

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41 changes: 36 additions & 5 deletions agents/dqn_agent.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import argparse
import sys
import time
import os
from stable_baselines3 import DQN
Expand Down Expand Up @@ -105,13 +107,42 @@ def train_dqn(total_timesteps=100_000, save_path=None, continue_training=False):
print(f"Training completed in {elapsed:.1f}s")
return model

def get_args():
"""
arguments parser in main function
python -m agents/*_agent.py -c -v -m -t 1200000
"""
parser = argparse.ArgumentParser()
parser.description='please enter optional parameters: train, visualize, continue, timestamp ...'
# add_argument:
# default: total_timesteps(20_000_000), do_train(true), do_visualize(true), continue_training(false)
parser.add_argument("-t", "--timesteps", help="total_timesteps for training", dest="total_timesteps", type=int, default=50_000_000)
parser.add_argument("-m", "--train", help="train mode", dest="do_train", action="store_false")
parser.add_argument("-v", "--visualize", help="visualize result", dest="do_visualize", action="store_false")
parser.add_argument("-c", "--continue", help="continue training or not", dest="continue_training", action="store_true")

# parser result
args = parser.parse_args()

return args

if __name__ == "__main__":
# Hyperparameters
total_timesteps = 50_000_000
continue_training = False
do_train = True
do_visualize = True
"""
Main function for agent training and visualizing
"""
# args parsing
args = get_args()
total_timesteps = args.total_timesteps
continue_training = args.continue_training
do_train = args.do_train
do_visualize = args.do_visualize

print(f'[INFO] Args:\n\t{total_timesteps=}\n\t{continue_training=}\n\t{do_train=}\n\t{do_visualize=}')
# user confirmation
user_confirm = input("[Note] Are you sure? (y/n) ")
if user_confirm.lower() == "n":
print("[WARNING] Please input the args again. Exiting !")
sys.exit(1)

if do_train:
trained = train_dqn(
Expand Down
44 changes: 39 additions & 5 deletions agents/dqn_masked_agent.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
import argparse
import os
import sys
import time
import numpy as np

Expand Down Expand Up @@ -133,12 +135,44 @@ def train_masked_dqn(total_timesteps: int = 500_000, continue_training: bool = F
return model


if __name__ == "__main__":
total_timesteps = 20_000_000
continue_training = False
do_train = True
do_visualize = True
def get_args():
"""
arguments parser in main function
python -m agents/dqn_*_agent.py -c -v -m -t 1200000
"""
parser = argparse.ArgumentParser()
parser.description='please enter optional parameters: train, visualize, continue, timestamp ...'
# add_argument:
# default: total_timesteps(20_000_000), do_train(true), do_visualize(true), continue_training(false)
parser.add_argument("-t", "--timesteps", help="total_timesteps for training", dest="total_timesteps", type=int, default=20_000_000)
parser.add_argument("-m", "--train", help="train mode", dest="do_train", action="store_false")
parser.add_argument("-v", "--visualize", help="visualize result", dest="do_visualize", action="store_false")
parser.add_argument("-c", "--continue", help="continue training or not", dest="continue_training", action="store_true")

# parser result
args = parser.parse_args()

return args

if __name__ == "__main__":
"""
Main function for agent training and visualizing
"""
# args parsing
args = get_args()
total_timesteps = args.total_timesteps
continue_training = args.continue_training
do_train = args.do_train
do_visualize = args.do_visualize

print(f'[INFO] Args:\n\t{total_timesteps=}\n\t{continue_training=}\n\t{do_train=}\n\t{do_visualize=}')
# user confirmation
user_confirm = input("[Note] Are you sure? (y/n) ")
if user_confirm.lower() == "n":
print("[WARNING] Please input the args again. Exiting !")
sys.exit(1)

# start to execute main process
if do_train:
train_masked_dqn(total_timesteps, continue_training)

Expand Down
39 changes: 38 additions & 1 deletion agents/ppo_agent.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import argparse
import os
import sys
from typing import Callable, Optional
Expand Down Expand Up @@ -95,13 +96,49 @@ def train_ppo(
print(f"[ppo] Training done: {final_path}.zip")
return model

def get_args():
"""
arguments parser in main function
python -m agents/dqn_*_agent.py -c -v -m -t 1200000
"""
parser = argparse.ArgumentParser()
parser.description='please enter optional parameters: train, visualize, continue, timestamp ...'
# add_argument:
# default: total_timesteps(20_000_000), do_train(true), do_visualize(true), continue_training(false)
parser.add_argument("-t", "--timesteps", help="total_timesteps for training", dest="total_timesteps", type=int, default=50_000_000)
parser.add_argument("-m", "--train", help="train mode", dest="do_train", action="store_false")
parser.add_argument("-v", "--visualize", help="visualize result", dest="do_visualize", action="store_false")
parser.add_argument("-c", "--continue", help="continue training or not", dest="continue_training", action="store_true")
parser.add_argument("-e", "--env", help="num of env", dest="num_envs", type=int, default=8)

# parser result
args = parser.parse_args()

return args

if __name__ == "__main__":
"""
Main function for agent training and visualizing
"""
# args parsing
args = get_args()
total_timesteps = args.total_timesteps
continue_training = args.continue_training
do_train = args.do_train
do_visualize = args.do_visualize
num_envs = args.num_envs

print(f'[INFO] Args:\n\t{total_timesteps=}\n\t{continue_training=}\n\t{do_train=}\n\t{do_visualize=}\n\t{num_envs=}')
# user confirmation
user_confirm = input("[Note] Are you sure? (y/n) ")
if user_confirm.lower() == "n":
print("[WARNING] Please input the args again. Exiting !")
sys.exit(1)

# Configuration
num_envs = 8
total_timesteps = 50_000_000
continue_training = False

do_train = True
do_visualize = True

Expand Down
44 changes: 37 additions & 7 deletions agents/ppo_masked_agent.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import argparse
import os
import sys
from typing import Callable, Optional
Expand Down Expand Up @@ -108,15 +109,44 @@ def train_masked_ppo(
print(f"[masked ppo] Training done: {final_path}.zip")
return model

def get_args():
"""
arguments parser in main function
python -m agents/*_agent.py -c -v -m -t 1200000
"""
parser = argparse.ArgumentParser()
parser.description='please enter optional parameters: train, visualize, continue, timestamp ...'
# add_argument:
# default: total_timesteps(20_000_000), do_train(true), do_visualize(true), continue_training(false)
parser.add_argument("-t", "--timesteps", help="total_timesteps for training", dest="total_timesteps", type=int, default=50_000_000)
parser.add_argument("-m", "--train", help="train mode", dest="do_train", action="store_false")
parser.add_argument("-v", "--visualize", help="visualize result", dest="do_visualize", action="store_false")
parser.add_argument("-c", "--continue", help="continue training or not", dest="continue_training", action="store_true")
parser.add_argument("-e", "--env", help="num of env", dest="num_envs", type=int, default=8)

# parser result
args = parser.parse_args()

return args

if __name__ == "__main__":
# Configuration
num_envs = 8
total_timesteps = 50_000_000
continue_training = False

do_train = True
do_visualize = True
"""
Main function for agent training and visualizing
"""
# args parsing
args = get_args()
total_timesteps = args.total_timesteps
continue_training = args.continue_training
do_train = args.do_train
do_visualize = args.do_visualize
num_envs = args.num_envs

print(f'[INFO] Args:\n\t{total_timesteps=}\n\t{continue_training=}\n\t{do_train=}\n\t{do_visualize=}\n\t{num_envs=}')
# user confirmation
user_confirm = input("[Note] Are you sure? (y/n) ")
if user_confirm.lower() == "n":
print("[WARNING] Please input the args again. Exiting !")
sys.exit(1)

if do_train:
pretrained = os.path.join(MODELS_DIR, "final_masked_ppo_model.zip")
Expand Down