def __init__(self, x_start, x_goal, heuristic_type):
self.u_set = motion_model.motions # feasible input set
self.xI, self.xG = x_start, x_goal
self.obs = env.obs_map() # position of obstacles
self.heuristic_type = heuristic_type
tools.show_map(self.xI, self.xG, self.obs, "a_star searching")
def __init__(self, x_start, x_goal):
self.u_set = motion_model.motions # feasible input set
self.xI, self.xG = x_start, x_goal
self.e = 0.001 # threshold for convergence
self.gamma = 0.9 # discount factor
self.obs = env.obs_map() # position of obstacles
self.lose = env.lose_map() # position of lose states
self.name1 = "Q-policy_iteration, e=" + str(self.e) \
+ ", gamma=" + str(self.gamma)
self.name2 = "convergence of error"
def __init__(self, x_start, x_goal):
self.u_set = motion_model.motions # feasible input set
self.xI, self.xG = x_start, x_goal
self.M = 500 # iteration numbers
self.gamma = 0.9 # discount factor
self.alpha = 0.5
self.epsilon = 0.1 # epsilon error
self.obs = env.obs_map() # position of obstacles
self.lose = env.lose_map() # position of lose states
self.name1 = "SARSA, M=" + str(self.M)
def __init__(self, x_start, x_goal):
self.u_set = motion_model.motions # feasible input set
self.xI, self.xG = x_start, x_goal
self.obs = env.obs_map() # position of obstacles
tools.show_map(self.xI, self.xG, self.obs, "breadth-first searching")