def train(args):
from algo import pposgd_simple, pposgd_origin
from nn import cnn_policy, cnn_lstm_policy, mlp_policy
import baselines.common.tf_util as U
rank = MPI.COMM_WORLD.Get_rank()
sess = U.single_threaded_session()
sess.__enter__()
task_name = "ppo." + args.taskname + "." + args.env_id.split(
"-")[0] + ".seed_" + ("%d" % args.seed)
args.log_dir = osp.join(args.log_dir, task_name)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = args.seed + 10000 * MPI.COMM_WORLD.Get_rank(
) if args.seed is not None else None
set_global_seeds(workerseed)
env = make_env(args.env_id,
seed=args.seed,
frame_stack=False,
save_camera=True,
save_path="../saved_camera",
no_cnn=False)()
def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
return cnn_policy.CnnPolicy(name,
ob_space,
ac_space,
hid_size=64,
num_hid_layers=1)
#return cnn_lstm_policy.CnnSenLSTMPolicy(name, ob_space, ac_space, hid_size=64, num_hid_layers = 1)
#return mlp_policy.MlpPolicy(name, ob_space, ac_space, hid_size=64, num_hid_layers = 1)
env.seed(workerseed)
pposgd_simple.learn(env,
policy_fn,
max_timesteps=int(args.num_timesteps * 1.1),
timesteps_per_actorbatch=256,
clip_param=0.2,
entcoeff=0.01,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
save_per_iter=100,
ckpt_dir=args.checkpoint_dir,
log_dir=args.log_dir,
task_name=task_name,
task=args.task,
load_model_path=args.load_model_path,
sample_stochastic=args.sample_stochastic)
env.close()
ckpt_dir = osp.join(args.checkpoint_dir, task_name)
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = args.seed + 10000 * MPI.COMM_WORLD.Get_rank(
) if args.seed is not None else None
set_global_seeds(workerseed)
args.no_cnn = True if args.policy_type == 'dense' else False
env = make_env(args.env_id,
seed=args.seed,
frame_stack=False,
save_camera=False,
remove_dyn=False,
no_cnn=args.no_cnn)()
policy = PPO(env.observation_space, env.action_space, args.policy_type, args)
seg_gen = traj_segment_generator(policy.pi,
env,
args.timesteps_per_actorbatch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
distbuffer = deque(maxlen=100)
ac1, vpred1 = self._act(stochastic, ob)
return ac1[0], vpred1[0]
def max_pool(img, k):
return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding = "SAME")
if __name__ == "__main__":
from env.env_util import make_env
import rospy
rospy.init_node("sample")
env = make_env("PipelineTrack-v1")()
pol = CnnPolicy("pi", env.observation_space, env.action_space, hid_size=256, num_hid_layers=1)
ob = env.reset()
sess = U.single_threaded_session()
sess.__enter__()
U.initialize()
a, v = pol.act(True, ob)
print(a)
print(v)