# model
self.evaluate(model_i)
if __name__ == '__main__':
config_i = config_instructor()
args = xworld_args.parser().parse_args()
args.visible_radius_unit_side = config_i.visible_radius_unit_side
args.visible_radius_unit_front = config_i.visible_radius_unit_front
args.ego_centric = config_i.ego_centric
args.map_config = config_i.map_config_file
args.goal_id = 0
args.israndom_goal = False
env_i = xworld_navi_goal_gt.XWorldNaviGoal(args)
config_p = config_planner()
args = xworld_args.parser().parse_args()
args.visible_radius_unit_side = config_p.visible_radius_unit_side
args.visible_radius_unit_front = config_p.visible_radius_unit_front
args.ego_centric = config_p.ego_centric
args.map_config = config_p.map_config_file
args.goal_id = 0
args.israndom_goal = False
env_p = xworld_navi_goal_gt.XWorldNaviGoal(args)
# load model
g_i = tf.Graph()
model_i = DRQN_instructor(env_i, config_i, g_i)
model_i.initialize()
# learning rate schedule
# model
self.evaluate(model_a)
if __name__ == '__main__':
config_a = config_agent()
args = xworld_args.parser().parse_args()
args.visible_radius_unit_side = config_a.visible_radius_unit_side
args.visible_radius_unit_front = config_a.visible_radius_unit_front
args.ego_centric = config_a.ego_centric
args.map_config = config_a.map_config_file
args.goal_id = 0
args.israndom_goal = False
env_a = xworld_navi_goal_obs_crop.XWorldNaviGoal(args)
config_i = config_planner()
args = xworld_args.parser().parse_args()
args.visible_radius_unit_side = config_i.visible_radius_unit_side
args.visible_radius_unit_front = config_i.visible_radius_unit_front
args.ego_centric = config_i.ego_centric
args.map_config = config_i.map_config_file
args.goal_id = 0
args.israndom_goal = False
env_i = xworld_navi_goal_gt_dnc.XWorldNaviGoal(args)
# load model
g_a = tf.Graph()
model_a = DRQN(env_a, config_a, g_a)
model_a.initialize()
# exploration strategy