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train_lcn.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.lcn.lcn import LCNTNDP, LCNTNDPModel
from morl_baselines.multi_policy.lcn.lcn_dst import LCNDST
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,
state_representation='multi_binary')
return env
env = make_env(args.gym_env)
if args.gym_env == 'deep-sea-treasure-concave-v0':
agent = LCNDST(
env,
scaling_factor=args.scaling_factor,
learning_rate=args.lr,
batch_size=args.batch_size,
project_name=args.project_name,
experiment_name=args.experiment_name,
log=not args.no_log,
seed=args.seed,
nr_layers=args.nr_layers,
hidden_dim=args.hidden_dim,
distance_ref=args.distance_ref,
lcn_lambda=args.lcn_lambda,
model_class=LCNTNDPModel
)
else:
agent = LCNTNDP(
env,
scaling_factor=args.scaling_factor,
learning_rate=args.lr,
batch_size=args.batch_size,
project_name=args.project_name,
experiment_name=args.experiment_name,
log=not args.no_log,
seed=args.seed,
nr_layers=args.nr_layers,
hidden_dim=args.hidden_dim,
distance_ref=args.distance_ref,
lcn_lambda=args.lcn_lambda,
model_class=LCNTNDPModel
)
if args.starting_loc is None:
print('NOTE: Training is running with random starting locations.')
save_dir = Path(f"./results/lcn_{args.env}_{datetime.datetime.today().strftime('%Y%m%d_%H_%M_%S.%f')}")
agent.train(
total_timesteps=args.timesteps,
eval_env=make_env(args.gym_env),
ref_point=args.ref_point,
num_er_episodes=args.num_er_episodes,
num_step_episodes=args.num_step_episodes,
max_buffer_size=args.max_buffer_size,
num_model_updates=args.num_model_updates,
starting_loc=args.starting_loc,
nr_stations=args.nr_stations,
max_return=args.max_return,
save_dir=save_dir,
pf_plot_limits=args.pf_plot_limits,
n_policies=args.num_policies,
cd_threshold=args.cd_threshold,
# known_pareto_front=env.unwrapped.pareto_front(gamma=1.0),
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MO LCN - 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('--lr', default=1e-2, type=float)
parser.add_argument('--batch_size', default=256, type=int)
parser.add_argument('--num_er_episodes', default=50, type=int)
parser.add_argument('--num_step_episodes', default=10, type=int)
parser.add_argument('--num_model_updates', default=10, type=int)
parser.add_argument('--num_policies', default=10, type=int)
parser.add_argument('--max_buffer_size', default=50, type=int)
parser.add_argument('--nr_layers', default=1, type=int)
parser.add_argument('--hidden_dim', default=64, type=int)
parser.add_argument('--timesteps', default=2000, type=int)
parser.add_argument('--no_log', action='store_true', default=False)
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--cd_threshold', default=0.2, type=float, help='controls the threshold for crowdedness distance.')
parser.add_argument('--distance_ref', default='nondominated', type=str, choices=['nondominated', 'optimal_max', 'nondominated_mean', 'interpolate', 'interpolate2', 'interpolate3'], help='controls the reference point for calculating the distance of every solution to the optimal point.')
parser.add_argument('--lcn_lambda', default=None, type=float, help='value between 0 and 1. Controls the size of the front to explore. lambda -> 1: full pareto front. lambda -> 0 full lorenz front.')
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 = "LCN-Dilemma"
args.scaling_factor = np.array([1, 1, 0.1])
args.ref_point = np.array([0, 0])
args.max_return=np.array([1, 1])
args.pf_plot_limits = [0, 0.5]
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 = "LCN-Margins"
args.scaling_factor = np.array([1, 1, 0.1])
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 = "LCN-Amsterdam"
args.scaling_factor = np.array([100] * args.nr_groups + [0.01])
args.ref_point = np.array([0] * args.nr_groups)
args.max_return=np.array([1] * args.nr_groups)
args.pf_plot_limits = None
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 = "LCN-Xian"
args.scaling_factor = np.array([100] * args.nr_groups + [0.01])
args.ref_point = np.array([0] * args.nr_groups)
args.max_return=np.array([1] * args.nr_groups)
args.pf_plot_limits = None
elif args.env == 'dst':
args.gym_env = 'deep-sea-treasure-concave-v0'
args.project_name = "DST"
args.experiment_name = "LCN-DST"
args.scaling_factor = np.array([0.1, 0.1, 0.01])
args.ref_point = np.array([0.0, -200.0])
args.max_return=np.array([124, -1])
args.pf_plot_limits = None
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)