def main():
parser = argparse.ArgumentParser(
description='PyTorch actor-critic example')
parser.add_argument('--hidden_layer_size',
type=int,
default=128,
metavar='N',
help='Hidden Layer Size (default: 128)')
parser.add_argument('--a_param',
type=float,
default=1.0,
metavar='G',
help='dynamics a_parameter')
parser.add_argument('--b_param',
type=float,
default=5.0,
metavar='G',
help='dynamics b_parameter')
args = parser.parse_args()
for i in range(1):
naf_environment = ENVIRONMENT(args, i)
naf_environment.run()
print("Learning Process Finished")
def main():
parser = argparse.ArgumentParser(description='PyTorch actor-critic example')
parser.add_argument('--hidden_layer_size', type=int, default=128, metavar='N',
help='Hidden Layer Size (default: 128)')
args = parser.parse_args()
for i in range(1):
naf_environment = ENVIRONMENT(args, i)
naf_environment.run()
def main():
#torch.utils.backcompat.broadcast_warning.enabled = True
#torch.utils.backcompat.keepdim_warning.enabled = True
#torch.set_default_tensor_type('torch.DoubleTensor')
parser = argparse.ArgumentParser(
description='PyTorch NAF-pendulum example')
parser.add_argument('--gamma',
type=float,
default=0.99,
metavar='G',
help='discount factor (default: 0.99)')
parser.add_argument('--tau',
type=float,
default=0.005,
metavar='G',
help='soft update parameter (default: 1e-3)')
parser.add_argument('--batch_size',
type=int,
default=128,
metavar='N',
help='Batch size (default: 128)')
parser.add_argument('--replay_buffer_size',
type=int,
default=1e6,
metavar='N',
help='Replay Buffer Size (default: 1e6)')
parser.add_argument('--hidden_layer_size',
type=int,
default=128,
metavar='N',
help='Hidden Layer Size (default: 64)')
parser.add_argument('--lr',
type=float,
default=5e-5,
metavar='G',
help='Learning rate of Actor Network (default: 1e-4)')
parser.add_argument('--max_episode',
type=float,
default=1,
metavar='N',
help='Max Episode (default: 200)')
parser.add_argument('--noise_scale',
type=float,
default=0.01,
metavar='G',
help='initial noise scale (default: 1.0)')
parser.add_argument('--final_noise_scale',
type=float,
default=0.01,
metavar='G',
help='final noise scale (default: 0.001)')
parser.add_argument('--a_param',
type=float,
default=0.95,
metavar='G',
help='a_param (default: 0.95)')
parser.add_argument('--b_param',
type=float,
default=25.5,
metavar='G',
help='b_param (default: 5.0~100.0)')
args = parser.parse_args()
for i in range(1):
SEED = 1
fixed_seed.fixed_seed_function(SEED)
naf_environment = ENVIRONMENT(args, i)
naf_environment.run()
print("Learning Process Finished")