def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, _ = train(args, extra_args)
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
while True:
actions = model.step(obs)[0]
obs, _, done, _ = env.step(actions)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
def main():
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
pickle_in = open("./tmp/make_model.pkl", "rb")
# pickle_in = open("./tmp/my_model","rb")
make_model = pickle.load(pickle_in)
model = make_model()
model.load("./tmp/my_model") #can use checkpoints
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
# print(obs)
while True:
actions = model.step(
obs
)[0] #0th are actions ... few more other array in step .. need to check for ppo
obs, _, done, _ = env.step(actions)
# env.render()
# done = done.any() if isinstance(done, np.ndarray) else done
done = done.all() if isinstance(done, np.ndarray) else done
print("step")
if done:
break
def main():
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: run.parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
train(args, extra_args)
def parse_cmdline_kwargs(args):
'''
convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
'''
def parse(v):
assert isinstance(v, str)
try:
return eval(v)
except (NameError, SyntaxError):
return v
return {k: parse(v) for k, v in parse_unknown_args(args).items()}
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
unknown_a = parse_unknown_args(unknown_args)
print('args')
print(args)
# The parser does not seem to accept new arguments, so I parse custom arguments here.
print('extra_args')
print(extra_args)
if 'progress_dir' in extra_args:
del extra_args['progress_dir']
print('Deleted progress_dir. new extra_arg:')
print(extra_args)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
if 'progress_dir' in unknown_a:
logger.configure(dir=unknown_a['progress_dir'])
else:
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, env = train(args, extra_args)
env.close()
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("Running trained model")
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 parse_cmdline_kwargs(args):
'''
convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
'''
def parse(v):
assert isinstance(v, str)
try:
return eval(v)
except (NameError, SyntaxError):
return v
return {k: parse(v) for k,v in parse_unknown_args(args).items()}
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
dir = "test2"
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure(dir)
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank
args.play = True
args.num_env = 3
#args.nsteps = 512
#print("ARGS IS ", args)
#print("extra_args IS ", extra_args)
print("")
print("RUNNING CORRECTLY")
model, _ = train(args, extra_args)
#args.save_path = "/Users/romc/Documents/RNN_exploation_learning/baselines/test/checkpoints/0001"
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
# init act model
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
s = model.initial_state
#print("initial state ", str(s))
m = [False for _ in range(1)]
print("obs is what " + str(obs))
step = 0
done = False
while done == False:
step = step + 1
print(" ")
print(" STEP " + str(step))
out = model.step(obs, S=s, M=m)
#print("out "+str(out))
#print("out[0] "+str(out[0]))
#print("out[1] "+str(out[1]))
#print("out[2] "+str(out[2]))
s = out[2]
m = out[3]
actions = out[0]
print("actions " + str(actions))
print(type(actions[0]))
#if step <5:
# continue
obs, _, done, _ = env.step(actions)
print(obs)
print(done)
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
unknown_a = parse_unknown_args(unknown_args)
print('args')
print(args)
# The parser does not seem to accept new arguments, so I parse custom arguments here.
print('extra_args')
print(extra_args)
if 'progress_dir' in extra_args:
del extra_args['progress_dir']
print('Deleted progress_dir. new extra_arg:')
print(extra_args)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
if 'progress_dir' in unknown_a:
logger.configure(dir=unknown_a['progress_dir'])
else:
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, env = train(args, extra_args)
#saver = tf.train.Saver()
#saver.save(model.sess, 'results/launch_balancer_04.sh/sess')
# #######
# logger.log("Test model")
# 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)
#
# actions, _, state, _ = model.step(obs,S=state, M=dones)
# obs, _, done, _ = env.step(actions)
#
# actions, _, state, _ = model.step(obs,S=state, M=dones)
# obs, _, done, _ = env.step(actions)
#
# print("Observations: ", obs)
# print("Action: ", actions)
# env.render()
# done = done.any() if isinstance(done, np.ndarray) else done
#
# if done:
# obs = env.reset()
#
# #####
# env.close()
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("Running trained model")
# 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)
#####
#import numpy
#from servorobots.network.agent_mlp import AgentMLP
#agent = AgentMLP("results/launch_balancer_04.sh/weight.weights", tf.nn.tanh)
with tf.Session() as sess:
#####
print(model.act_model)
while True:
actions, _, state, _ = model.step(obs, S=state, M=dones)
# writer = tf.summary.FileWriter("results/launch_balancer_04.sh/grahp", sess.graph)
#print("From checkpoint: ", actions1)
#states = numpy.reshape([0,0,0,0,0,0,0,0], 8)
#actions = agent.act(sess, numpy.reshape(states, 8))
#print("From weight file: ", actions)
obs, _, done, _ = env.step(actions)
print('Observation: ' + str(obs[0][3]) + ' Actions: ' +
str(actions))
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
env.close()
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
arg_parser.add_argument('--options_play', help='Agent play with options',
default=False, action='store_true')
arg_parser.add_argument(
'--selective_option_play', default=False, action='store_true',
help='Agent play with selective option')
args, unknown_args = arg_parser.parse_known_args()
extra_args = {k: parse(v) for k, v in parse_unknown_args(unknown_args).items()}
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, _ = train(args, extra_args)
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
w, h, _ = obs[0].shape
video = MovieWriter(osp.join(logger.get_dir(), "play.mp4"), (w, h), 2)
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
import cv2
cv2.imwrite(os.path.join(logger.get_dir(), "init_ob.png"),
obs[0][:, :, ::-1])
i = 0
while i < 1000:
i += 1
actions = model.step(obs, stochastic=True)[0]
obs, _, done, _ = env.step(actions)
# env.render()
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
# done = done.any() if isinstance(done, np.ndarray) else done
done = done[0]
if done:
obs = env.reset()
env.close()
video.close()
break
if args.selective_option_play:
logger.log("Running selective option play with trained options policy")
env = build_env(args)
obs = env.reset()
w, h, _ = obs[0].shape
video = MovieWriter(
osp.join(logger.get_dir(), "selective_option_play.mp4"), (w, h), 2)
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
import cv2
cv2.imwrite(os.path.join(logger.get_dir(), "init_ob.png"),
obs[0][:, :, ::-1])
i = 0
while i < 1000:
i += 1
actions = model.selective_option_step(obs, stochastic=True)[0]
obs, _, done, _ = env.step(actions)
# env.render()
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
# done = done.any() if isinstance(done, np.ndarray) else done
done = done[0]
if done:
obs = env.reset()
env.close()
video.close()
break
if args.options_play:
logger.log("Running trained options policy")
video_path = osp.join(logger.get_dir(), "options_play")
if not osp.exists(video_path):
os.mkdir(video_path)
# assume 64 options
for i in range(64):
env = build_env(args)
obs = env.reset()
w, h, _ = obs[0].shape
logger.log("Create op_play_{}.mp4".format(i))
video = MovieWriter(
osp.join(video_path, "op_play_{}.mp4".format(i)), (w, h), 2)
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
option_z = np.zeros((env.num_envs, 64))
option_z[:, i] = 1.0
step = 1
while step < 1000:
actions = model.option_step(option_z, obs, stochastic=True)[0]
discri = model.option_select(obs)[0]
logger.log("step: {} discriminator: {}".format(step, discri))
obs, _, done, _ = env.step(actions)
video.add_frame(np.array(obs[0][:, :, ::-1], dtype=np.uint8))
done = done[0]
step += 1
if done:
obs = env.reset()
env.close()
video.close()
break
def main(args=None):
# configure logger, disable logging in child MPI processes (with rank > 0)
if args is None:
from thesis_galljamov18.python.training.guro_train import LOAD_MODEL
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
"""All args: {'nsteps': 2048, 'nminibatches': 32, 'lam': 0.95, 'gamma': 0.99, 'noptepochs': 10, 'log_interval': 1,
'ent_coef': 0.0, 'lr': <function mujoco.<locals>.<lambda> at 0x7f8f5af49f28>, 'cliprange': 0.2, 'value_network': 'copy'}"""
# train my environment instead default one
args.env = "Guro-v0"
args.num_timesteps = 0 if LOAD_MODEL else 10e6 + 1e5
args.play = LOAD_MODEL
args.alg = 'ppo2'
args.network = 'mlp'
# change further arguments
# nsteps = 2048
# nminibatches = 32
# gamma = 0.95
# lr = 0.001
# cliprange = 0.2
# extra_args.update({'nsteps': nsteps, 'nminibatches': nminibatches, 'gamma': gamma, 'cliprange': cliprange})
# extra_args.update({'lr': 1e-10})
else:
extra_args = {}
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, _ = train(args, extra_args)
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("\n------------\nRunning trained model\n------------\n")
def say(text):
os.system(
'spd-say "{}" --volume -1 --voice-type male2'.format(text))
# say("Attention please! Running trained model in 10 seconds!")
# import time
# time.sleep(10)
env = build_env(args)
obs = env.reset()
#env.ob_rms.mean = [0,0,0,0,0,0] #[0., 0.39362465587763634, 0., -0.11370739423088674, 0.01929697539211253, 0.5066570016460371]
# [ 0, 0.46073392, 0, 0.20411958, -0.05412459, 0.49079091]
# print("\n----------\nOBSERV_MEANS of loaded model: " + str(env.ob_rms.mean) + "\n----------\n")
# exit(33)
while True:
actions = model.step(obs)[0]
obs, _, done, _ = env.step(actions)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
