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 | acktr | a2t | resnet')
    parser.add_argument('--lr', type=float, default=7e-4,
                        help='learning rate (default: 7e-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='value loss coefficient (default: 0.5)')
    parser.add_argument('--seed', type=int, default=1,
                        help='random seed (default: 1)')
    parser.add_argument('--num-processes', type=int, default=16,
                        help='how many training CPU processes to use (default: 16)')
    parser.add_argument('--num-steps', type=int, default=20,
                        help='number of forward steps in A2C (default: 20)')
    parser.add_argument('--ppo-epoch', type=int, default=4,
                        help='number of ppo epochs (default: 4)')
    parser.add_argument('--batch-size', type=int, default=64,
                        help='ppo batch size (default: 64)')
    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=1,
                        help='number of frames to stack (default: 1)')
    parser.add_argument('--log-interval', type=int, default=100,
                        help='log interval, one log per n updates (default: 100)')
    parser.add_argument('--save-interval', type=int, default=100,
                        help='save interval, one save per n updates (default: 100)')
    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=10e6,
                        help='number of frames to train (default: 10e6)')
    parser.add_argument('--env-name', default='VizDoom',
                        help='environment to train on (default: VizDoom)')
    parser.add_argument('--config-path', default='./scenarios/basic.cfg',
                        help='vizdoom configuration file path (default: ./scenarios/basic.cfg)')
    parser.add_argument('--source-models-path', default='./trained_models',
                        help='directory from where to load source task models [A2T only] (default: ./trained_models)')
    parser.add_argument('--log-dir', default='/tmp/vizdoom/',
                        help='directory to save agent logs (default: /tmp/vizdoom)')
    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('--no-vis', action='store_true', default=False,
                        help='disables visdom visualization')
    args = parser.parse_args()

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

    return args