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import argparse
def ParseInput():
parser = argparse.ArgumentParser()
parser.add_argument('--train', action='store_true', help='enable training')
""" ---------- Simulation Parameters ---------- """
parser.add_argument('--minN', type=int, default=5, dest='minN', help='minimumN')
parser.add_argument('--numN', type=int, default=20, dest='numN', help='numberN')
parser.add_argument('--divN', type=int, default=5, dest='divN', help='divisionN')
parser.add_argument('--maxSeed', type=int, default=25, dest='maxSeed', help='numberSeed')
args = parser.parse_args()
""" ---------- Algorithm Hyperparameters ------- """
args.num_actions = 2
args.num_states = 10
args.J = 10 ** 2 # Number of iterations for training the neural network based policy
args.L = 10 ** 2
args.run_eval = 10 ** 2 # Number of iterations for evaluating a policy
args.gamma = 0.9 # Discount factor
""" --------- Reward Parameters --------- """
args.alpha_r = 1
args.beta_r = 0.5
args.lambda_r = 0.5
"""----------- Learning Parameters --------- """
args.alpha = 10**-3
args.eta = 10**-3
args.hidden_size = 32
return args