# NODES = sorted([236, 237, 186, 170, 141, 162, 140, 238, 142, 229, 239, 48, 161, 107, 263, 262, 234, 68, 100, 143])
# NODES = sorted([236, 237, 186, 170, 141])
NODES = sorted([236, 237, 186, 170, 141, 162, 140, 238, 142, 229])
initial_vehicle = args.init_veh
iterations = args.iter
vehicle_speed = args.veh_speed
if file_conf is not None:
NODES = sorted(file_conf.pop("nodes", NODES))
initial_vehicle = int(file_conf.pop("init_veh", initial_vehicle))
iterations = int(file_conf.pop("iter", iterations))
vehicle_speed = int(file_conf.pop("veh_speed", vehicle_speed))
update_graph_file(os.path.join(CONFIG, 'gps.csv'),
os.path.join(CONFIG, 'aam.csv'), NODES)
update_vehicle_initial_distribution(veh_dist=[initial_vehicle] *
len(NODES),
nodes=NODES,
speed=vehicle_speed)
ray.init()
nodes_list = [str(x) for x in NODES]
configure = ppo.DEFAULT_CONFIG.copy()
# configure['vf_share_layers'] = False
configure['env'] = TaxiRebalance
configure['num_workers'] = args.num_cpu if args.num_cpu is not None else 1
configure['num_gpus'] = args.num_gpu if args.num_gpu is not None else 0
configure['vf_clip_param'] = args.vf_clip
configure['lr'] = args.lr
configure['train_batch_size'] = args.tr_bat_size
configure['rollout_fragment_length'] = args.wkr_smpl_size
configure['sgd_minibatch_size'] = args.sgd_bat_size
configure['env_config'] = {
type=float,
default=3e-3)
parser.add_argument('--e_lr',
nargs='?',
metavar='<entropy learning rate>',
type=float,
default=3e-3)
args = parser.parse_args()
# NODES = sorted(pd.read_csv(os.path.join(CONFIG, 'aam.csv'), index_col=0, header=0).index.values.tolist())
# NODES = sorted([236, 237, 186, 170, 141, 162, 140, 238, 142, 229, 239, 48, 161, 107, 263, 262, 234, 68, 100, 143])
NODES = sorted([236, 237, 186, 170, 141])
update_graph_file(os.path.join(CONFIG, 'gps.csv'),
os.path.join(CONFIG, 'aam.csv'), NODES)
update_vehicle_initial_distribution(
[int(args.init_veh) for i in range(len(NODES))], nodes=NODES)
ray.init()
with open(graph_file, 'r') as f:
node_list = json.load(f)
node_list = [x for x in node_list]
configure = sac.DEFAULT_CONFIG.copy()
configure['env'] = TaxiRebalance
try:
with open(args.config, 'r') as file:
file_conf = json.load(file)
except FileNotFoundError:
configure['num_workers'] = args.num_cpu
configure['num_gpus'] = args.num_gpu
configure['timesteps_per_iteration'] = 300 # MDP steps per iteration