arguments.py

import argparse

import torch


def get_args():
    parser = argparse.ArgumentParser(description='RL')
    parser.add_argument('--algo', default='a2c',
                        help='algorithm to use: a2c | ppo | acktr')
    parser.add_argument('--lr', type=float, default=1e-4,
                        help='learning rate (default: 1e-4)')
    parser.add_argument('--eps', type=float, default=1e-5,
                        help='RMSprop optimizer epsilon (default: 1e-5)')
    parser.add_argument('--alpha', type=float, default=0.99,
                        help='RMSprop optimizer apha (default: 0.99)')
    parser.add_argument('--gamma', type=float, default=0.99,
                        help='discount factor for rewards (default: 0.99)')
    parser.add_argument('--use-gae', action='store_true', default=False,
                        help='use generalized advantage estimation')
    parser.add_argument('--tau', type=float, default=0.95,
                        help='gae parameter (default: 0.95)')
    parser.add_argument('--entropy-coef', type=float, default=0.01,
                        help='entropy term coefficient (default: 0.01)')
    parser.add_argument('--value-loss-coef', type=float, default=0.5,
                        help='value loss coefficient (default: 0.5)')
    parser.add_argument('--max-grad-norm', type=float, default=0.5,
                        help='max norm of gradients (default: 0.5)')
    parser.add_argument('--seed', type=int, default=1,
                        help='random seed (default: 1)')
    parser.add_argument('--num-processes', type=int, default=8,
                        help='how many training CPU processes to use (default: 8)')
    parser.add_argument('--num-steps', type=int, default=5,
                        help='number of forward steps in A2C (default: 5)')
    parser.add_argument('--ppo-epoch', type=int, default=4,
                        help='number of ppo epochs (default: 4)')
    parser.add_argument('--num-mini-batch', type=int, default=32,
                        help='number of batches for ppo (default: 32)')
    parser.add_argument('--clip-param', type=float, default=0.2,
                        help='ppo clip parameter (default: 0.2)')
    parser.add_argument('--num-stack', type=int, default=4,
                        help='number of frames to stack (default: 4)')
    parser.add_argument('--log-interval', type=int, default=10,
                        help='log interval, one log per n updates (default: 10)')
    parser.add_argument('--save-interval', type=int, default=100*3,
                        help='save interval, one save per n updates (default: 10)')
    parser.add_argument('--vis-interval', type=int, default=100,
                        help='vis interval, one log per n updates (default: 100)')
    parser.add_argument('--num-frames', type=int, default=100*1e6,
                        help='number of frames to train (default: 10e6)')
    parser.add_argument('--env-name', nargs='*', default=['PongNoFrameskip-v4'],
                        help='environment to train on (default: PongNoFrameskip-v4)')
    parser.add_argument('--log-dir', default='./tmp/gym/',
                        help='directory to save agent logs (default: /tmp/gym)')
    parser.add_argument('--save-dir', default='./trained_models/',
                        help='directory to save agent logs (default: ./trained_models/)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--recurrent-policy', action='store_true', default=False,
                        help='use a recurrent policy')
    parser.add_argument('--att', help="Attention policy", choices=['spatial', 'temporal', 'None'], default='None')
    parser.add_argument('--no-vis', action='store_true', default=True,
                        help='disables visdom visualization')

    parser.add_argument('--load_model', default=None,
                        help='directory to save agent logs (default: None)')

    parser.add_argument('--transfer', action='store_true', default=False,
                        help='Transfer policy')


    args = parser.parse_args()

    args.cuda = not args.no_cuda and torch.cuda.is_available()
    args.vis = not args.no_vis
    print(args.vis)

    if(args.att):
        print("###########################################################################\n")
        print("REMEMBER TO SET LEARNING RATE TO 1e-4 SINCE YOU ARE USING ATTENTION NETWORK\n")
        print("###########################################################################\n")
        print(args.lr," <-------> LEARNING RATE ")
    return args