def main(args):
maze_id = args.maze_id
GazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)
print("Generating data for env maze{}".format(maze_id))
file_number = args.file_number
total_episodes = args.total_episodes
# random_generated_int = np.random.randint(0, 2 ** 31 - 1)
# np.random.seed(random_generated_int)
time_steps = args.time_steps
for i in range(file_number):
total_frames = 0
obs_data = []
action_data = []
episode = 0
while episode < total_episodes:
print('---------------------------')
obs_sequence = []
action_sequence = []
observation = GazeboMaze.reset()
done = False
t = 0
while t < time_steps:
action = dict()
action['linear_vel'] = np.random.uniform(0, 1)
action['angular_vel'] = np.random.uniform(-1, 1)
# print(action)
obs_sequence.append(observation)
action_sequence.append(
[action['linear_vel'], action['angular_vel']])
t += 1
observation, done, reward = GazeboMaze.execute(action)
total_frames += t
episode += 1
obs_data.append(obs_sequence)
action_data.append(action_sequence)
print("{}th collections".format(i))
print("Episode {} finished after {} timesteps".format(episode, t))
print("Current dataset contains {} observations".format(
total_frames))
print("Saving dataset ...")
np.save(dir_name + '/observation' + str(i), obs_data)
np.save(dir_name + '/action' + str(i), action_data)
GazeboMaze.close()
from tensorforce.agents import PPOAgent
import os
import itertools
import config
import env
import worldModels.VAE
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
maze_id = config.maze_id
deterministic = config.deterministic
restore = False
GazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)
record_dir = 'record'
if not os.path.exists(record_dir):
os.makedirs(record_dir)
saver_dir = './models/nav{}'.format(maze_id)
if not os.path.exists(saver_dir):
os.makedirs(saver_dir)
summarizer_dir = './record/PPO_remap/nav{}'.format(maze_id)
if not os.path.exists(summarizer_dir):
os.makedirs(summarizer_dir)
vae = worldModels.VAE.VAE()
vae.set_weights(config.vae_weight)
from tensorforce.agents import DQNAgent
import os
import env
import config
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
maze_id = config.maze_id
restore = False
GazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=False)
record_dir = 'record'
if not os.path.exists(record_dir):
os.makedirs(record_dir)
saver_dir = './models/nav{}'.format(maze_id)
if not os.path.exists(saver_dir):
os.makedirs(saver_dir)
summarizer_dir = './record/E2E_DQN/nav{}'.format(maze_id)
if not os.path.exists(summarizer_dir):
os.makedirs(summarizer_dir)
states = dict(image=GazeboMaze.states,
previous_act=dict(shape=(2, ), type='float'),
relative_pos=dict(shape=(2, ), type='float'))
network_spec = [[
dict(type='input', names=['image']),
dict(type='conv2d',