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train_gpipd.py
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import datetime
from pathlib import Path
import random
import mo_gymnasium as mo_gym
from motndp.city import City
from motndp.constraints import MetroConstraints
import numpy as np
import torch
import envs
import argparse
from morl_baselines.multi_policy.gpi_pd.gpi_pd_tndp import GPILS
from morl_baselines.multi_policy.gpi_pd.gpi_pd import GPILS as GPILSDST
def main(args):
def make_env(gym_env):
if gym_env == 'deep-sea-treasure-concave-v0':
return mo_gym.make(gym_env)
city = City(
args.city_path,
groups_file=args.groups_file,
ignore_existing_lines=args.ignore_existing_lines
)
env = mo_gym.make(args.gym_env,
city=city,
constraints=MetroConstraints(city),
nr_stations=args.nr_stations,
starting_loc=args.starting_loc,
state_representation='multi_binary')
return env
env = make_env(args.gym_env)
eval_env = make_env(args.gym_env)
if args.gym_env == 'deep-sea-treasure-concave-v0':
agent = GPILSDST(
env,
num_nets=1,
max_grad_norm=None,
learning_rate=args.learning_rate,
gamma=1,
batch_size=args.batch_size,
net_arch=args.net_arch,
buffer_size=int(args.buffer_size),
initial_epsilon=1.0,
final_epsilon=0.05,
epsilon_decay_steps=args.epsilon_decay_steps,
learning_starts=args.learning_starts,
alpha_per=0.6,
min_priority=0.01,
per=False,
use_gpi=True,
gradient_updates=args.gradient_updates,
target_net_update_freq=args.target_update_freq,
tau=1,
real_ratio=0.5,
log=True,
project_name="DST",
# experiment_name=args.experiment_name,
seed = args.seed
)
else:
agent = GPILS(
env,
num_nets=1,
max_grad_norm=None,
learning_rate=args.learning_rate,
gamma=1,
batch_size=args.batch_size,
net_arch=args.net_arch,
buffer_size=int(args.buffer_size),
initial_epsilon=1.0,
final_epsilon=0.05,
epsilon_decay_steps=args.epsilon_decay_steps,
learning_starts=args.learning_starts,
alpha_per=0.6,
min_priority=0.01,
per=False,
use_gpi=True,
gradient_updates=args.gradient_updates,
target_net_update_freq=args.target_update_freq,
tau=1,
real_ratio=0.5,
log=True,
project_name="MORL-TNDP",
experiment_name=args.experiment_name,
action_mask_dim=8,
seed = args.seed
)
if args.starting_loc is None:
print('NOTE: Training is running with random starting locations.')
save_dir = Path(f"./results/gpi_{args.env}_{datetime.datetime.today().strftime('%Y%m%d_%H_%M_%S.%f')}")
agent.train(
total_timesteps=args.total_timesteps,
eval_env=eval_env,
ref_point=args.ref_point,
num_eval_weights_for_front=args.num_eval_weights_for_front,
# known_pareto_front=env.unwrapped.pareto_front(gamma=0.98),
weight_selection_algo='gpi-ls',
timesteps_per_iter=args.timesteps_per_iter,
eval_freq=args.eval_freq,
eval_mo_freq=args.eval_mo_freq
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MO GPI-LS - TNDP")
# Acceptable values: 'dilemma', 'margins', 'amsterdam', 'dst'
parser.add_argument('--env', default='dilemma', type=str)
# For amsterdam environment we have different groups files (different nr of objectives)
parser.add_argument('--nr_groups', default=5, type=int)
# Starting location of the agent
parser.add_argument('--starting_loc_x', default=None, type=int)
parser.add_argument('--starting_loc_y', default=None, type=int)
# Episode horizon -- used as a proxy of both the budget and the number of stations (stations are not really costed)
parser.add_argument('--nr_stations', type=int, required=True)
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--hidden_dim', default=64, type=int)
parser.add_argument('--nr_layers', default=2, type=int)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--timesteps_per_iter', default=5000, type=int)
parser.add_argument('--total_timesteps', default=30000, type=int)
parser.add_argument('--epsilon_decay_steps', default=20000, type=int)
parser.add_argument('--buffer_size', default=4096, type=int)
parser.add_argument('--learning_rate', default=1e-3, type=float)
parser.add_argument('--target_update_freq', default=20, type=int)
parser.add_argument('--gradient_updates', default=2, type=int)
args = parser.parse_args()
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
random.seed(args.seed)
# Some values are hardcoded for each environment (this is flexible, but we don't want to have to pass 100 arguments to the script)
if args.env == 'dilemma':
args.city_path = Path(f"./envs/mo-tndp/cities/dilemma_5x5")
args.nr_stations = 9
args.gym_env = 'motndp_dilemma-v0'
args.project_name = "MORL-TNDP"
args.groups_file = "groups.txt"
args.ignore_existing_lines = True
args.experiment_name = "GPI-LS-Dilemma"
args.ref_point = np.array([0, 0])
args.max_return=np.array([1, 1])
args.pf_plot_limits = [0, 0.5]
# Override everything with the best settings for the dilemma environment.
args.net_arch = [64, 64]
args.batch_size = 32
args.timesteps_per_iter = 100
args.total_timesteps = 3000
args.epsilon_decay_steps = 1500
args.num_eval_weights_for_front = 100
args.buffer_size = 512
args.learning_starts = 50
args.learning_rate = 1e-5
args.target_update_freq = 100
args.gradient_updates = 5
args.eval_freq = 100
args.eval_mo_freq = 100
elif args.env == 'margins':
args.city_path = Path(f"./envs/mo-tndp/cities/margins_5x5")
args.nr_stations = 9
args.gym_env = 'motndp_margins-v0'
args.project_name = "MORL-TNDP"
args.groups_file = f"groups.txt"
args.ignore_existing_lines = True
args.experiment_name = "GPI-LS-Margins"
args.ref_point = np.array([0, 0])
args.max_return=np.array([1, 1])
args.pf_plot_limits = [0, 0.5]
elif args.env == 'amsterdam':
args.city_path = Path(f"./envs/mo-tndp/cities/amsterdam")
args.gym_env = 'motndp_amsterdam-v0'
args.project_name = "MORL-TNDP"
args.groups_file = f"price_groups_{args.nr_groups}.txt"
args.ignore_existing_lines = True
args.experiment_name = "GPI-LS-Amsterdam"
args.ref_point = np.array([0] * args.nr_groups)
args.max_return=np.array([1] * args.nr_groups)
args.pf_plot_limits = None
args.starting_loc_x = 9
args.starting_loc_y = 19
args.num_eval_weights_for_front = 100
args.learning_starts = 100
args.eval_freq = args.timesteps_per_iter
args.eval_mo_freq = args.timesteps_per_iter
args.net_arch = [args.hidden_dim] * args.nr_layers
elif args.env == 'xian':
args.city_path = Path(f"./envs/mo-tndp/cities/xian")
args.gym_env = 'motndp_xian-v0'
args.project_name = "MORL-TNDP"
args.groups_file = f"price_groups_{args.nr_groups}.txt"
args.ignore_existing_lines = True
args.experiment_name = "GPI-LS-Xian"
args.ref_point = np.array([0] * args.nr_groups)
args.max_return=np.array([1] * args.nr_groups)
args.pf_plot_limits = None
args.starting_loc_x = 9
args.starting_loc_y = 19
args.num_eval_weights_for_front = 100
args.learning_starts = 100
args.eval_freq = args.timesteps_per_iter
args.eval_mo_freq = args.timesteps_per_iter
args.net_arch = [args.hidden_dim] * args.nr_layers
elif args.env == 'dst':
args.gym_env = 'deep-sea-treasure-concave-v0'
args.project_name = "DST"
args.experiment_name = "GPI-LS-DST"
args.ref_point = np.array([0.0, -200.0])
args.max_return = np.array([124, -1])
args.pf_plot_limits = [0, 0.5]
args.net_arch = [args.hidden_dim] * args.nr_layers
args.learning_starts = 50
args.num_eval_weights_for_front = 100
args.eval_freq = args.timesteps_per_iter
args.eval_mo_freq = args.timesteps_per_iter
if args.starting_loc_x is not None and args.starting_loc_y is not None:
args.starting_loc = (args.starting_loc_x, args.starting_loc_y)
else:
args.starting_loc = None
main(args)