def __init__(
self, init_n_ego_dict
): # init_n_ego_dict is used to init traffic (mainly) and ego dynamics
self.TASK2MODEL = dict(
left=LoadPolicy('../utils/models/left', 100000),
straight=LoadPolicy('../utils/models/straight', 95000),
right=LoadPolicy('../utils/models/right', 145000),
)
self.n_ego_instance = {}
self.n_ego_dynamics = {}
self.n_ego_select_index = {}
for egoID, ego_dict in init_n_ego_dict.items():
self.n_ego_instance[egoID] = CrossroadEnd2end(
training_task=NAME2TASK[egoID[:2]], display=True)
self.mpp = MultiPathGenerator()
self.virtual_model = dict(left=EnvironmentModel(training_task='left',
mode='selecting'),
straight=EnvironmentModel(
training_task='straight',
mode='selecting'),
right=EnvironmentModel(training_task='right',
mode='selecting'))
self.reset(init_n_ego_dict)
def __init__(self, init_n_ego_dict): # init_n_ego_dict is used to init traffic (mainly) and ego dynamics
self.TASK2MODEL = dict(left=LoadPolicy('../utils/models/left/experiment-2021-03-15-16-39-00', 180000),
straight=LoadPolicy('../utils/models/straight/experiment-2021-03-15-19-16-13', 175000),
right=LoadPolicy('../utils/models/right/experiment-2021-03-15-21-02-51', 195000),)
self.n_ego_instance = {}
self.n_ego_dynamics = {}
self.n_ego_select_index = {}
for egoID, ego_dict in init_n_ego_dict.items():
self.n_ego_instance[egoID] = CrossroadEnd2end(training_task=NAME2TASK[egoID[:2]],
mode='testing',
multi_display=True)
self.mpp = MultiPathGenerator()
self.virtual_model = dict(left=EnvironmentModel(training_task='left', mode='selecting'),
straight=EnvironmentModel(training_task='straight', mode='selecting'),
right=EnvironmentModel(training_task='right', mode='selecting'))
# ------------------build graph for tf.function in advance-----------------------
for task in ['left', 'straight', 'right']:
env = CrossroadEnd2end(training_task=task, mode='testing', multi_display=False)
for i in range(3):
obs = env.reset()
obs = tf.convert_to_tensor(obs[np.newaxis, :], dtype=tf.float32)
self.is_safe(obs, i, task)
obs = env.reset()
obs_with_specific_shape = np.tile(obs, (3, 1))
self.TASK2MODEL[task].run_batch(obs_with_specific_shape)
self.TASK2MODEL[task].obj_value_batch(obs_with_specific_shape)
env.close()
# ------------------build graph for tf.function in advance-----------------------
self.reset(init_n_ego_dict)
def __init__(self, task, train_exp_dir, ite, logdir=None):
self.task = task
self.policy = LoadPolicy('../utils/models/{}/{}'.format(task, train_exp_dir), ite)
self.env = CrossroadEnd2end(training_task=self.task, mode='testing')
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = MultiPathGenerator()
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
if self.logdir is not None:
config = dict(task=task, train_exp_dir=train_exp_dir, ite=ite)
with open(self.logdir + '/config.json', 'w', encoding='utf-8') as f:
json.dump(config, f, ensure_ascii=False, indent=4)
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.old_index = 0
self.path_list = self.stg.generate_path(self.task)
# ------------------build graph for tf.function in advance-----------------------
for i in range(3):
obs = self.env.reset()
obs = tf.convert_to_tensor(obs[np.newaxis, :], dtype=tf.float32)
self.is_safe(obs, i)
obs = self.env.reset()
obs_with_specific_shape = np.tile(obs, (3, 1))
self.policy.run_batch(obs_with_specific_shape)
self.policy.obj_value_batch(obs_with_specific_shape)
# ------------------build graph for tf.function in advance-----------------------
self.reset()
def __init__(
self,
training_task, # 'left', 'straight', 'right'
num_future_data=0,
display=False,
**kwargs):
metadata = {'render.modes': ['human']}
self.dynamics = VehicleDynamics()
self.interested_vehs = None
self.training_task = training_task
self.ref_path = ReferencePath(self.training_task, **kwargs)
self.detected_vehicles = None
self.all_vehicles = None
self.ego_dynamics = None
self.num_future_data = num_future_data
self.env_model = EnvironmentModel(training_task, num_future_data)
self.init_state = {}
self.action_number = 2
self.exp_v = EXPECTED_V #TODO: temp
self.ego_l, self.ego_w = L, W
self.action_space = gym.spaces.Box(low=-1,
high=1,
shape=(self.action_number, ),
dtype=np.float32)
self.seed()
self.v_light = None
self.step_length = 100 # ms
self.step_time = self.step_length / 1000.0
self.init_state = self._reset_init_state()
self.obs = None
self.action = None
self.veh_mode_dict = VEHICLE_MODE_DICT[self.training_task]
self.veh_num = VEH_NUM[self.training_task]
self.virtual_red_light_vehicle = False
self.done_type = 'not_done_yet'
self.reward_info = None
self.ego_info_dim = None
self.per_tracking_info_dim = None
self.per_veh_info_dim = None
if not display:
self.traffic = Traffic(self.step_length,
mode='training',
init_n_ego_dict=self.init_state,
training_task=self.training_task)
self.reset()
action = self.action_space.sample()
observation, _reward, done, _info = self.step(action)
self._set_observation_space(observation)
plt.ion()
def __init__(self, task):
self.task = task
if self.task == 'left':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\left', 100000)
elif self.task == 'right':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\right', 145000)
elif self.task == 'straight':
self.policy = LoadPolicy('G:\\env_build\\utils\\models\\straight', 95000)
self.horizon = 25
self.num_future_data = 0
self.env = CrossroadEnd2end(training_task=self.task, num_future_data=self.num_future_data)
self.model = EnvironmentModel(self.task)
self.obs = self.env.reset()
self.stg = StaticTrajectoryGenerator_origin(mode='static_traj')
self.data2plot = []
self.mpc_cal_timer = TimerStat()
self.adp_cal_timer = TimerStat()
self.recorder = Recorder()
def __init__(self, task, logdir=None):
self.task = task
if self.task == 'left':
self.policy = LoadPolicy('../utils/models/left', 100000)
elif self.task == 'right':
self.policy = LoadPolicy('../utils/models/right', 145000)
elif self.task == 'straight':
self.policy = LoadPolicy('../utils/models/straight', 95000)
self.env = CrossroadEnd2end(training_task=self.task)
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = None
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.reset()
def __init__(self, task, train_exp_dir, ite, logdir=None):
self.task = task
self.policy = LoadPolicy(
'../utils/models/{}/{}'.format(task, train_exp_dir), ite)
self.env = CrossroadEnd2end(training_task=self.task)
self.model = EnvironmentModel(self.task, mode='selecting')
self.recorder = Recorder()
self.episode_counter = -1
self.step_counter = -1
self.obs = None
self.stg = None
self.step_timer = TimerStat()
self.ss_timer = TimerStat()
self.logdir = logdir
if self.logdir is not None:
config = dict(task=task, train_exp_dir=train_exp_dir, ite=ite)
with open(self.logdir + '/config.json', 'w',
encoding='utf-8') as f:
json.dump(config, f, ensure_ascii=False, indent=4)
self.fig = plt.figure(figsize=(8, 8))
plt.ion()
self.hist_posi = []
self.reset()