from ifqi import envs
import argparse, random
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--path',
type=str,
default='data/breakout/model.h5',
help='path to the hdf5 weights file for the autoencoder')
parser.add_argument('--original', action='store_true')
parser.add_argument('--rebuilt', action='store_true')
parser.add_argument('--convmap', action='store_true')
args = parser.parse_args()
env = envs.Atari('BreakoutDeterministic-v3')
AE = Autoencoder((4 * 84 * 84, ), load_path=args.path)
state0 = env.reset()
# Move base to the left
state0, reward, done, info = env.step(3)
state00, reward, done, info = env.step(3) # Base moves one step wrt state 0
# Start the game
state1, reward, done, info = env.step(1)
# Keep base to the left and move only the ball
for i in range(10):
state1, reward, done, info = env.step(3)
state2, reward, done, info = env.step(3) # Ball moves one step wrt state 1
args = parser.parse_args()
logger = Logger(
debug=args.debug,
output_folder='../ifqi/algorithms/selection/feature_extraction/output/',
)
# Redirect stdout
if args.log:
old_stdout = sys.stdout
sys.stdout = open(logger.path + 'output_dump.txt', 'w', 0)
# Create environment
# TODO Enduro, MsPacman, Qbert
mdp = envs.Atari(name=args.env)
# Feature extraction model (used for collecting episodes and evaluation)
AE = Autoencoder((4 * 84 * 84, ), load_path=args.path)
# Read or collect dataset
if args.dataset is not None:
# Load from disk if dataset was provided
print('Loading dataset at %s' % args.dataset)
dataset = np.loadtxt(args.dataset, delimiter=',', skiprows=1)
else:
print('Collecting episodes using model at %s' % args.path)
collection_params = {
'episodes': args.episodes,
'env_name': args.env,
'header': None,
'minimum_score': args.min_score,
'onehot': args.onehot,
'video': False,
from ifqi.envs.gridworld import GridWorldEnv
from helpers import crop_state
from PIL import Image
from ifqi import envs
import argparse, random
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='data/breakout/model.h5', help='path to the hdf5 weights file for the autoencoder')
parser.add_argument('--original', action='store_true')
parser.add_argument('--rebuilt', action='store_true')
parser.add_argument('--convmap', action='store_true')
args = parser.parse_args()
env = envs.Atari('BreakoutDeterministic-v3')
AE = Autoencoder((4 * 84 * 84,), load_path=args.path)
state0 = env.reset()
# Move base to the left
state0, reward, done, info = env.step(3)
state00, reward, done, info = env.step(3) # Base moves one step wrt state 0
# Start the game
state1, reward, done, info = env.step(1)
# Keep base to the left and move only the ball
for i in range(10):
state1, reward, done, info = env.step(3)
state2, reward, done, info = env.step(3) # Ball moves one step wrt state 1
parser.add_argument('-d', '--debug', action='store_true', help='run in debug mode (no output files)')
parser.add_argument('-v', '--video', action='store_true', help='display video output')
parser.add_argument('--njobs', type=int, default=1, help='number of processes to use. Don\'t set this flag w/ GPU.')
parser.add_argument('--env', type=str, default='BreakoutDeterministic-v3', help='Atari environment to run')
parser.add_argument('--episodes', type=int, default=1000, help='number of episodes to run')
parser.add_argument('--path', type=str, default='data/model.h5', help='path to the hdf5 weights file for the AE')
parser.add_argument('-e', '--encode', action='store_true', help='save a SARS dataset with the encoded features')
parser.add_argument('-i', '--images', action='store_true', help='save images of states and a SARS csv with ids')
parser.add_argument('--min-score', type=int, default=0, help='keep episode only if it got more than this score')
parser.add_argument('--onehot', action='store_true', help='save actions in the dataset with onehot encoding')
args = parser.parse_args()
logger = Logger(debug=args.debug)
if args.encode:
AE = Autoencoder((4 * 84 * 84,), load_path=args.path)
# Automatically generate headers from the output length of AE.flat_encode
nb_states = AE.flat_encode(np.expand_dims(np.ones(AE.input_shape), axis=0)).shape[0]
nb_actions = envs.Atari(args.env).action_space.n
actions_header = ['A%s' % i for i in xrange(nb_actions)] if args.onehot else ['A0']
header = ['S%s' % i for i in xrange(nb_states)] + actions_header + ['R'] + \
['SS%s' % i for i in xrange(nb_states)] + ['Absorbing', 'Finished']
# Collect episodes
dataset = collect_encoded_dataset(AE,
episodes=args.episodes,
env_name=args.env,
header=header,
onehot=args.onehot,
minimum_score=args.min_score,