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")