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main.py
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main.py
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"""
Main function for training and evaluating MARL algorithms in traffic envs
@author: Tianshu Chu
"""
import argparse
import configparser
import logging
import tensorflow as tf
import threading
from envs.large_grid_env import LargeGridEnv, LargeGridController
from agents.models import IA2C, IA2C_FP, IA2C_CU, MA2C_NC, MA2C_IC3, MA2C_DIAL
from utils import (Counter, Trainer, Tester, Evaluator,
check_dir, copy_file, find_file,
init_dir, init_log, init_test_flag,
plot_evaluation, plot_train)
def parse_args():
default_base_dir = '/Users/tchu/Documents/rl_test/deeprl_dist/ma2c_ic3_test'
default_config_dir = './config/config_ma2c_ic3.ini'
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str, required=False,
default=default_base_dir, help="experiment base dir")
subparsers = parser.add_subparsers(dest='option', help="train or evaluate")
sp = subparsers.add_parser('train', help='train a single agent under base dir')
sp.add_argument('--test-mode', type=str, required=False,
default='after_train_test',
help="test mode during training",
choices=['no_test', 'in_train_test', 'after_train_test', 'all_test'])
sp.add_argument('--config-dir', type=str, required=False,
default=default_config_dir, help="experiment config path")
sp = subparsers.add_parser('evaluate', help="evaluate and compare agents under base dir")
sp.add_argument('--evaluate-seeds', type=str, required=False,
default=','.join([str(i) for i in range(2000, 2500, 10)]),
help="random seeds for evaluation, split by ,")
args = parser.parse_args()
if not args.option:
parser.print_help()
exit(1)
return args
def init_env(config, port=0, naive_policy=False):
if not naive_policy:
return LargeGridEnv(config, port=port)
else:
env = LargeGridEnv(config, port=port)
policy = LargeGridController(env.node_names)
return env, policy
def init_agent(env, config, total_step, seed):
if env.agent == 'ia2c':
return IA2C(env.n_s_ls, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
elif env.agent == 'ia2c_fp':
return IA2C_FP(env.n_s_ls, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
elif env.agent == 'ma2c_nc':
return MA2C_NC(env.n_s, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
elif env.agent == 'ma2c_ic3':
return MA2C_IC3(env.n_s, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
elif env.agent == 'ma2c_cu':
return IA2C_CU(env.n_s, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
elif env.agent == 'ma2c_dial':
return MA2C_DIAL(env.n_s, env.n_a, env.neighbor_mask, env.distance_mask, env.coop_gamma,
total_step, config, seed=seed)
else:
return None
def train(args):
base_dir = args.base_dir
dirs = init_dir(base_dir)
init_log(dirs['log'])
config_dir = args.config_dir
copy_file(config_dir, dirs['data'])
config = configparser.ConfigParser()
config.read(config_dir)
in_test, post_test = init_test_flag(args.test_mode)
# init env
env = init_env(config['ENV_CONFIG'])
logging.info('Training: a dim %d, agent dim: %d' % (env.n_a, env.n_agent))
# init step counter
total_step = int(config.getfloat('TRAIN_CONFIG', 'total_step'))
test_step = int(config.getfloat('TRAIN_CONFIG', 'test_interval'))
log_step = int(config.getfloat('TRAIN_CONFIG', 'log_interval'))
global_counter = Counter(total_step, test_step, log_step)
# init centralized or multi agent
seed = config.getint('ENV_CONFIG', 'seed')
model = init_agent(env, config['MODEL_CONFIG'], total_step, seed)
# disable multi-threading for safe SUMO implementation
summary_writer = tf.summary.FileWriter(dirs['log'])
trainer = Trainer(env, model, global_counter, summary_writer, in_test, output_path=dirs['data'])
trainer.run()
# save model
final_step = global_counter.cur_step
logging.info('Training: save final model at step %d ...' % final_step)
model.save(dirs['model'], final_step)
# post-training test
if post_test:
test_dirs = init_dir(base_dir, pathes=['eva_data'])
evaluator = Evaluator(env, model, test_dirs['eva_data'])
evaluator.run()
def evaluate_fn(agent_dir, output_dir, seeds, port):
agent = agent_dir.split('/')[-1]
if not check_dir(agent_dir):
logging.error('Evaluation: %s does not exist!' % agent)
return
# load config file for env
config_dir = find_file(agent_dir + '/data/')
if not config_dir:
return
config = configparser.ConfigParser()
config.read(config_dir)
# init env
env, greedy_policy = init_env(config['ENV_CONFIG'], port=port, naive_policy=True)
env.init_test_seeds(seeds)
# load model for agent
if agent != 'greedy':
# init centralized or multi agent
model = init_agent(env, config['MODEL_CONFIG'], 0, 0)
if model is None:
return
if not model.load(agent_dir + '/model/'):
return
else:
model = greedy_policy
# collect evaluation data
evaluator = Evaluator(env, model, output_dir)
evaluator.run()
def evaluate(args):
base_dir = args.base_dir
dirs = init_dir(base_dir, pathes=['eva_data', 'eva_log'])
init_log(dirs['eva_log'])
# enforce the same evaluation seeds across agents
seeds = args.evaluate_seeds
logging.info('Evaluation: random seeds: %s' % seeds)
if not seeds:
seeds = []
else:
seeds = [int(s) for s in seeds.split(',')]
evaluate_fn(base_dir, dirs['eva_data'], seeds, 1)
if __name__ == '__main__':
args = parse_args()
if args.option == 'train':
train(args)
else:
evaluate(args)