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arguments.py
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arguments.py
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import argparse
import torch
def get_args():
parser = argparse.ArgumentParser(description='RL')
# the saving directory for train.py
parser.add_argument(
'--output_dir', type=str, default='trained_models/my_model')
# resume training from an existing checkpoint or not
parser.add_argument(
'--resume', default=False, action='store_true')
# if resume = True, load from the following checkpoint
parser.add_argument(
'--load-path', default='trained_models/GST_predictor_non_rand/checkpoints/41200.pt',
help='path of weights for resume training')
parser.add_argument(
'--overwrite',
default=True,
action='store_true',
help = "whether to overwrite the output directory in training")
parser.add_argument(
'--num_threads',
type=int,
default=1,
help="number of threads used for intraop parallelism on CPU")
# only implement in testing
parser.add_argument(
'--phase', type=str, default='test')
parser.add_argument(
'--cuda-deterministic',
action='store_true',
default=False,
help="sets flags for determinism when using CUDA (potentially slow!)")
# only works for gpu only (although you can make it work on cpu after some minor fixes)
parser.add_argument(
'--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--seed', type=int, default=425, help='random seed (default: 1)')
parser.add_argument(
'--num-processes',
type=int,
default=16,
help='how many training processes to use (default: 16)')
parser.add_argument(
'--num-mini-batch',
type=int,
default=2,
help='number of batches for ppo (default: 32)')
parser.add_argument(
'--num-steps',
type=int,
default=30,
help='number of forward steps in A2C (default: 5)')
parser.add_argument(
'--recurrent-policy',
action='store_true',
default=True,
help='use a recurrent policy')
parser.add_argument(
'--ppo-epoch',
type=int,
default=5,
help='number of ppo epochs (default: 4)')
parser.add_argument(
'--clip-param',
type=float,
default=0.2,
help='ppo clip parameter (default: 0.2)')
parser.add_argument(
'--value-loss-coef',
type=float,
default=0.5,
help='value loss coefficient (default: 0.5)')
parser.add_argument(
'--entropy-coef',
type=float,
default=0.0,
help='entropy term coefficient (default: 0.01)')
parser.add_argument(
'--lr', type=float, default=4e-5, 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(
'--max-grad-norm',
type=float,
default=0.5,
help='max norm of gradients (default: 0.5)')
# 10e6 for holonomic, 20e6 for unicycle
parser.add_argument(
'--num-env-steps',
type=int,
default=20e6,
help='number of environment steps to train (default: 10e6)')
# True for unicycle, False for holonomic
parser.add_argument(
'--use-linear-lr-decay',
action='store_true',
default=False,
help='use a linear schedule on the learning rate')
parser.add_argument(
'--algo', default='ppo', help='algorithm to use: a2c | ppo | acktr')
parser.add_argument(
'--save-interval',
type=int,
default=200,
help='save interval, one save per n updates (default: 100)')
parser.add_argument(
'--use-gae',
action='store_true',
default=True,
help='use generalized advantage estimation')
parser.add_argument(
'--gae-lambda',
type=float,
default=0.95,
help='gae lambda parameter (default: 0.95)')
parser.add_argument(
'--log-interval',
type=int,
default=20,
help='log interval, one log per n updates (default: 10)')
parser.add_argument(
'--use-proper-time-limits',
action='store_true',
default=False,
help='compute returns taking into account time limits')
# for srnn only
# RNN size
parser.add_argument('--human_node_rnn_size', type=int, default=128,
help='Size of Human Node RNN hidden state')
parser.add_argument('--human_human_edge_rnn_size', type=int, default=256,
help='Size of Human Human Edge RNN hidden state')
parser.add_argument(
'--aux-loss',
action='store_true',
default=False,
help='auxiliary loss on human nodes outputs')
# Input and output size
parser.add_argument('--human_node_input_size', type=int, default=3,
help='Dimension of the node features')
parser.add_argument('--human_human_edge_input_size', type=int, default=2,
help='Dimension of the edge features')
parser.add_argument('--human_node_output_size', type=int, default=256,
help='Dimension of the node output')
# Embedding size
parser.add_argument('--human_node_embedding_size', type=int, default=64,
help='Embedding size of node features')
parser.add_argument('--human_human_edge_embedding_size', type=int, default=64,
help='Embedding size of edge features')
# Attention vector dimension
parser.add_argument('--attention_size', type=int, default=64,
help='Attention size')
# Sequence length
parser.add_argument('--seq_length', type=int, default=30,
help='Sequence length')
# use self attn in human states or not
parser.add_argument('--use_self_attn', type=bool, default=True,
help='Attention size')
# use attn between humans and robots or not (todo: implment this in network models)
parser.add_argument('--use_hr_attn', type=bool, default=True,
help='Attention size')
# No prediction: for orca, sf, old_srnn, selfAttn_srnn_noPred ablation: 'CrowdSimVarNum-v0',
# for constant velocity Pred, ground truth Pred: 'CrowdSimPred-v0'
# gst pred: 'CrowdSimPredRealGST-v0'
parser.add_argument(
'--env-name',
default='CrowdSimPredRealGST-v0',
help='name of the environment')
# sort all humans and squeeze them to the front or not
parser.add_argument('--sort_humans', type=bool, default=True)
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