def main():
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
args.num_timesteps = 0
args.play = True
args.env = 'YamaXRealForwardWalk-v0'
model, env = train(args, extra_args)
env.close()
env = build_env(args)
obs = env.reset()
def initialize_placeholders(nlstm=128, **kwargs):
return np.zeros((args.num_env or 1, 2 * nlstm)), np.zeros((1))
state, dones = initialize_placeholders(**extra_args)
while True:
actions, _, state, _ = model.step(obs, S=state, M=dones)
obs, _, done, _ = env.step(actions)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
env.close()
def load_model(path, render=False):
model, env = train(args, extra_args)
model.load(path)
if render: #play
obs = env.reset()
score = 0
done = False
while done == False:
# Get the action
actions, values, _ = model.step(obs)
# Take actions in env and look the results
obs, rewards, done, info = env.step(actions)
score += rewards
env.render()
print("Score ", score)
env.close()
return model, env
def setUp(env, alg, load_path):
args = Bunch({
'env': env,
'alg': alg,
'num_timesteps': 0,
'seed': None,
'num_env': 1,
'network': None
})
extra_args = {'load_path': load_path}
model, env = train(args, extra_args)
env.close()
env = build_env(args, extra_args)
return env, model
def nm_main(env, env_type, seed, alg, num_timesteps, network, gamestate,
num_env, reward_scale, save_path, save_video_interval,
save_video_length, play, log_path, env_args, alg_args):
bl_args = bl_arg_class()
bl_args.env = env
bl_args.env_type = env_type
bl_args.seed = seed
bl_args.alg = alg
bl_args.num_timesteps = num_timesteps
bl_args.network = network
bl_args.gamestate = gamestate
bl_args.num_env = num_env
bl_args.reward_scale = reward_scale
bl_args.save_path = save_path
bl_args.save_video_interval = save_video_interval
bl_args.save_video_length = save_video_length
bl_args.log_path = log_path
bl_args.play = play
bl_args.env_args = env_args
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
blr.configure_logger(bl_args.log_path)
else:
rank = MPI.COMM_WORLD.Get_rank()
blr.configure_logger(bl_args.log_path, format_strs=[])
model, env = blr.train(bl_args, alg_args)
#if save_path is not None and rank == 0:
# save_path = os.path.expanduser(save_path)
# model.save(save_path)
env.close()
print('\nDie allerallerallerletzte Zeile...\n')
def main():
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
args.num_env = 1
extra_args = parse_cmdline_kwargs(unknown_args)
model, env = train(args, extra_args)
env.close()
logger.log("Running trained model")
env = build_env(args)
if not args.play:
ts = time.gmtime()
directory = time.strftime("./render/%s", ts)
logger.log("Output video to directory:", directory)
env.envs = [gym.wrappers.Monitor(env.envs[0], directory=directory)]
obs = env.reset()
def initialize_placeholders(nlstm=128, **kwargs):
return np.zeros((args.num_env, 2 * nlstm)), np.zeros((1))
state, dones = initialize_placeholders(**extra_args)
NUM_VIDEO = 1
while True:
actions, _, state, _ = model.step(obs, S=state, M=dones)
obs, _, done, _ = env.step(actions)
if args.play:
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
NUM_VIDEO -= 1
if NUM_VIDEO <= 0:
break
obs = env.reset()
env.close()
import numpy as np
import os
import shutil
from argparse import Namespace
from baselines.run import build_env, train, parse_cmdline_kwargs
from baselines.a2c.a2c import Model
from baselines.common.cmd_util import common_arg_parser
print("Running trained model")
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
model, env = train(args, extra_args)
# Load the data to test on
data = np.load('test_images.npy')
tests = len(data)
# Build environment
env = build_env(args)
env.envs[0].env.env.phase = 'test'
# Load model
model.load(args.env + args.alg)
# Test model on all images in dataset
path = "/tmp/movements"
norm_steps = []
fail = 0
# for j in range(100):