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")