import gym
import numpy as np
from datetime import datetime
from com.modulabs.ctrl.utils.FileUtils import FileUtils
from com.modulabs.ctrl.utils.SeedNumbers import RandomSeeder
from com.modulabs.ctrl.agents.CNNAgent import CNNAgent
RandomSeeder.set_seed()
RandomSeeder.reset()
GYM_ENV_NAME = 'Pong-v0'
EPISODES = 1000
model_name = 'pong'
cache_dir = 'caches/' + model_name
timestamp = datetime.now().strftime("%Y%m%d_%H%M")
def get_model_dir(_model_name, _timestamp):
_model_dir = 'models/{}/dqn-{}.h5'.format(_model_name, _timestamp)
print("get_model_dir({})".format(_model_dir))
return _model_dir
def train(_model_name, _timestamp, _model_dir, _render):
env = gym.make(GYM_ENV_NAME)
agent = CNNAgent(env,
name=_model_name,
timestamp=_timestamp,
train_start=50000)
global_step = 0
import gym
import numpy as np
from datetime import datetime
from com.modulabs.ctrl.utils.FileUtils import FileUtils
from com.modulabs.ctrl.utils.SeedNumbers import RandomSeeder
from com.modulabs.ctrl.agents.DQNAgent import DQNAgent
RandomSeeder.set_seed()
# RandomSeeder.reset()
GYM_ENV_NAME = 'CartPole-v1'
EPISODES = 1000
timestamp = datetime.now().strftime("%Y%m%d_%H%M")
model_name = 'cartpole'
model_path = 'models/{}/dqn-{}.h5'.format(model_name, timestamp)
cache_dir = 'caches/' + model_name
def train():
global model_name, timestamp
env = gym.make(GYM_ENV_NAME)
agent = DQNAgent(env, name=model_name, timestamp=timestamp, use_swa=True)
global_step = 0
size_of_window = 15
scores = []
if FileUtils.exists_file(model_path):
agent.load_weights(model_path)
for e in range(EPISODES):
def flip_random_seed(on=True):
from com.modulabs.ctrl.utils.SeedNumbers import RandomSeeder
if on:
RandomSeeder.set_seed()
else:
RandomSeeder.reset()