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rl_train_boxflipup.py
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rl_train_boxflipup.py
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import argparse
import gym
import os
from stable_baselines3 import A2C, PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.env_util import make_vec_env
gym.envs.register(id="BoxFlipUp-v0",
entry_point="manipulation.envs.box_flipup:BoxFlipUpEnv")
parser = argparse.ArgumentParser(
description='Install ToC and Navigation into book html files.')
parser.add_argument('--test', action='store_true')
args = parser.parse_args()
observations = "state"
time_limit = 10 if not args.test else 0.5
zip = "data/box_flipup_ppo_{observations}.zip"
log = "/tmp/ppo_box_flipup/"
if __name__ == '__main__':
num_cpu = 48 if not args.test else 2
env = make_vec_env("BoxFlipUp-v0",
n_envs=num_cpu,
seed=0,
vec_env_cls=SubprocVecEnv,
env_kwargs={
'observations': observations,
'time_limit': time_limit,
})
# env = "BoxFlipUp-v0"
if args.test:
model = PPO('MlpPolicy', env, n_steps=4, n_epochs=2, batch_size=8)
elif os.path.exists(zip):
model = PPO.load(zip, env, verbose=1, tensorboard_log=log)
else:
model = PPO('MlpPolicy', env, verbose=1, tensorboard_log=log)
new_log = True
while True:
model.learn(total_timesteps=100000 if not args.test else 4,
reset_num_timesteps=new_log)
if args.test:
break
model.save(zip)
new_log = False