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arguments.py
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arguments.py
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import argparse
import torch
import pdb
def get_args():
parser = argparse.ArgumentParser(description='RL')
parser.add_argument('--dataset_path', type=str, help="The path of the environments where we test")
parser.add_argument('--mode', type=str, default='full', choices=['simple', 'full', 'check', 'check_neurips'], help='Environment type')
parser.add_argument('--load-model', type=str, default='',
help='whether the model is loaded')
parser.add_argument('--num-per-apartment', type=int, default=3, help='Maximum #episodes/apartment')
parser.add_argument(
'--algo', default='a2c', help='algorithm to use: a2c | ppo | acktr')
parser.add_argument(
'--task_type', default='find', choices=['find', 'full', 'open', 'put'], help='algorithm to use')
parser.add_argument(
'--lr', type=float, default=7e-4, help='learning rate (default: 7e-4)')
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(
'--gae-lambda',
type=float,
default=0.95,
help='gae lambda parameter (default: 0.95)')
parser.add_argument(
'--entropy-coef',
type=float,
default=0.0,
help='entropy term coefficient (default: 0.0)')
parser.add_argument(
'--init-epsilon',
type=float,
default=0.1,
help='initial epsilon (default: 0.1)')
parser.add_argument(
'--final-epsilon',
type=float,
default=0.01,
help='final epsilon (default: 0.01)')
parser.add_argument(
'--max-exp-episodes',
type=int,
default=10000,
help='Maximum exploration episodes (default: 10000)')
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(
'--cuda-deterministic',
action='store_true',
default=False,
help="sets flags for determinism when using CUDA (potentially slow!)")
parser.add_argument(
'--evaluation',
action='store_true',
default=False,
help="wheter to evaluate")
parser.add_argument(
'--t-max',
type=int,
default=20,
help='number of bptt steps (default: 20)')
parser.add_argument(
'--long-log',
type=int,
default=10,
help='log interval, one log per n updates (default: 10)')
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,
help='save interval, one save per n updates (default: 100)')
parser.add_argument(
'--eval-interval',
type=int,
default=None,
help='eval interval, one eval per n updates (default: None)')
parser.add_argument(
'--num-env-steps',
type=int,
default=10e6,
help='number of environment steps to train (default: 10e6)')
parser.add_argument(
'--log-dir',
default='logs/',
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(
'--use-proper-time-limits',
action='store_true',
default=False,
help='compute returns taking into account time limits')
parser.add_argument(
'--recurrent-policy',
action='store_true',
default=True,
help='use a recurrent policy')
parser.add_argument(
'--num-frame-stack',
type=int,
default=1,
help='number of frames to stack in the observations')
parser.add_argument(
'--obs_type',
type=str,
default='partial',
choices=['full', 'rgb', 'visibleid', 'partial'],
)
parser.add_argument(
'--agent_type',
type=str,
default='hrl_mcts',
choices=['rl', 'hrl_mcts'],
)
parser.add_argument(
'--use-linear-lr-decay',
action='store_true',
default=False,
help='use a linear schedule on the learning rate')
parser.add_argument(
'--attention_type',
type=str,
default='linear',
choices=['fc', 'dot', 'linear'],
help='use a linear schedule on the learning rate')
parser.add_argument(
'--base_net',
default='TF',
choices=['GNN', 'CNN', 'TF'],
help='use a linear schedule on the learning rate')
parser.add_argument(
'--train_mode',
default='RL',
choices=['BC', 'RL']
)
parser.add_argument(
'--hidden-size',
type=int,
default=128,
help='network dim')
parser.add_argument(
'--nb_episodes',
type=int,
default=10000,
help='number of episodes')
parser.add_argument(
'--replay_start',
type=int,
default=20,
help='when to start using the replay buffer')
parser.add_argument(
'--batch_size',
type=int,
default=2,
help='batch size for replay buffer')
parser.add_argument(
'--t_max',
type=int,
default=100,
help='number of steps until breaking bptt')
parser.add_argument(
'--max-episode-length',
type=int,
default=200,
help='number of episodes')
parser.add_argument(
'--max-number-steps',
type=int,
default=25,
help='number of episodes')
parser.add_argument(
'--balanced_sample',
action='store_true',
default=False,
help='what')
parser.add_argument(
'--neg_ratio',
type=float,
default=0.5,
help='ratio with 0 reward')
parser.add_argument(
'--max-num-edges',
type=int,
default=300,
help='how many objects in observation space')
parser.add_argument(
'--max-num-objects',
type=int,
default=150,
help='how many objects in observation space')
parser.add_argument(
'--task-set',
type=str,
default='full',
)
parser.add_argument(
'--max_gradient_norm', type=int, default=10)
parser.add_argument(
'--memory-capacity-episodes', type=int, default=2000)
parser.add_argument('--no-time-normalization', action='store_true', default=False,
help='whether to normalize loss on time')
parser.add_argument('--on-policy', action='store_true', default=False,
help='whether to run on or off policy')
parser.add_argument('--add-timestep', action='store_true', default=False,
help='add timestep to observations')
parser.add_argument('--teleport', action='store_true', default=False,
help='teleport')
parser.add_argument('--tensorboard-logdir', default='log_tb',
help='logs to tensorboard in the specified directory')
# Exec args
parser.add_argument(
'--executable_file', type=str,
default='../executable/linux_exec_v3.x86_64')
parser.add_argument(
'--base-port', type=int, default=8080)
parser.add_argument(
'--display', type=str, default="2")
parser.add_argument(
'--env-name',
default='virtualhome',
help='environment to train on (default: PongNoFrameskip-v4)')
parser.add_argument(
'--simulator-type',
default='unity',
choices=['unity', 'python'],
help='whether to use unity or python sim')
parser.add_argument(
'--num-processes',
type=int,
default=1,
help='how many training CPU processes to use (default: 1)')
parser.add_argument('--use-editor', action='store_true', default=False,
help='whether to use an editor or executable')
parser.add_argument('--debug', action='store_true', default=False,
help='debugging mode')
parser.add_argument('--logging', action='store_true', default=False,
help='debugging mode')
parser.add_argument('--use-gt-actions', action='store_true', default=False,
help='debugging mode')
parser.add_argument('--use-alice', action='store_true', default=False,
help='debugging mode')
parser.add_argument('--num_steps_mcts', type=int, default=25,
help='how many steps to take of the given plan')
parser.add_argument('--c_loss_close', type=float, default=0., help='coefficient auxiliary loss goal close')
parser.add_argument('--c_loss_goal', type=float, default=0., help='coefficient auxiliary loss goal obj')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
assert args.algo in ['a2c', 'ppo'], \
'Recurrent policy is not implemented for ACKTR'
return args
if __name__ == '__main__':
args = get_args()