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https://github.com/zhm-real/PathPlanning.git
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zhm-real
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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@author: huiming zhou
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"""
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import env
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import tools
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import motion_model
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import matplotlib.pyplot as plt
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import numpy as np
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import sys
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class Q_value_iteration:
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def __init__(self, x_start, x_goal):
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self.u_set = motion_model.motions # feasible input set
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self.xI, self.xG = x_start, x_goal
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self.e = 0.001 # threshold for convergence
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self.gamma = 0.9 # discount factor
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self.obs = env.obs_map() # position of obstacles
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self.lose = env.lose_map() # position of lose states
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self.name1 = "Q-value_iteration, e=" + str(self.e) + ", gamma=" + str(self.gamma)
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self.name2 = "convergence of error"
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def iteration(self):
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Q_table = {}
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policy = {}
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delta = sys.maxsize
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for i in range(env.x_range):
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for j in range(env.y_range):
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if (i, j) not in self.obs:
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Q_table[(i, j)] = [0, 0, 0, 0]
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while delta > self.e:
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x_value = 0
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for x in Q_table:
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if x not in x_Goal:
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for k in range(len(self.u_set)):
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[x_next, p_next] = motion_model.move_prob(x, self.u_set[k], self.obs)
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Q_value = self.cal_Q_value(x_next, p_next, Q_table)
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v_diff = abs(Q_table[x][k] - Q_value)
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Q_table[x][k] = Q_value
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if v_diff > 0:
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x_value = max(x_value, v_diff)
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delta = x_value
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for x in Q_table:
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if x not in x_Goal:
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policy[x] = np.argmax(Q_table[x])
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return policy
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def simulation(self, xI, xG, policy):
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path = []
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x = xI
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while x not in xG:
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u = self.u_set[policy[x]]
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x_next = (x[0] + u[0], x[1] + u[1])
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if x_next not in self.obs:
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x = x_next
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path.append(x)
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path.pop()
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return path
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def animation(self, path):
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plt.figure(1)
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tools.show_map(self.xI, self.xG, self.obs, self.lose, self.name1)
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for x in path:
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tools.plot_dots(x)
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plt.show()
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def cal_Q_value(self, x, p, table):
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value = 0
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reward = self.get_reward(x)
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for i in range(len(x)):
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value += p[i] * (reward[i] + self.gamma * max(table[x[i]]))
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return value
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def get_reward(self, x_next):
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reward = []
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for x in x_next:
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if x in self.xG:
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reward.append(10)
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elif x in self.lose:
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reward.append(-10)
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else:
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reward.append(0)
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return reward
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if __name__ == '__main__':
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x_Start = (5, 5)
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x_Goal = [(49, 5), (49, 25)]
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QVI = Q_value_iteration(x_Start, x_Goal)
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policy_QVI = QVI.iteration()
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path_VI = QVI.simulation(x_Start, x_Goal, policy_QVI)
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QVI.animation(path_VI)
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