from examples.utils.utils import get_policy
tensorboard_folder = './tensorboard/MouseWalkingMaze/base/'
model_folder = './models/MouseWalkingMaze/base/'
if not os.path.isdir(tensorboard_folder):
os.makedirs(tensorboard_folder)
if not os.path.isdir(model_folder):
os.makedirs(model_folder)
policy = ''
model_tag = ''
if len(sys.argv) > 1:
policy = sys.argv[1]
model_tag = '_' + sys.argv[1]
env = DummyVecEnv([lambda: BaseEnv()])
model = PPO2(get_policy(policy),
env,
verbose=0,
nminibatches=1,
tensorboard_log=tensorboard_folder)
model.learn(total_timesteps=25000, tb_log_name='PPO2' + model_tag)
model.save(model_folder + "PPO2" + model_tag)
del model
model = PPO2.load(model_folder + "PPO2" + model_tag)
done = False
states = None
obs = env.reset()
from examples.utils.utils import get_policy
tensorboard_folder = './tensorboard/MouseWalkingMaze/base/'
model_folder = './models/MouseWalkingMaze/base/'
if not os.path.isdir(tensorboard_folder):
os.makedirs(tensorboard_folder)
if not os.path.isdir(model_folder):
os.makedirs(model_folder)
policy = ''
model_tag = ''
if len(sys.argv) > 1:
policy = sys.argv[1]
model_tag = '_' + sys.argv[1]
env = DummyVecEnv([lambda: BaseEnv(map_name='map1')])
model = A2C(get_policy(policy),
env,
verbose=0,
tensorboard_log=tensorboard_folder)
model.learn(total_timesteps=2500000, tb_log_name='A2C_map1' + model_tag)
model.save(model_folder + "A2C_map1" + model_tag)
del model
model = A2C.load(model_folder + "A2C_map1" + model_tag)
done = False
states = None
obs = env.reset()
def setup_function():
pytest.env = BaseEnv(map_name='map1', end_step=100)