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NearOptimalLocalPolicy/Scripts/Parameters.py
Go to file| 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 |