가치이터레이션 코드

environment.py

 

import tkinter as tk
import time
import numpy as np
import random
from PIL import ImageTk, Image
 
PhotoImage = ImageTk.PhotoImage
UNIT = 100  # 픽셀 수
HEIGHT = 5  # 그리드월드 세로
WIDTH = 5  # 그리드월드 가로
TRANSITION_PROB = 1
POSSIBLE_ACTIONS = [0, 1, 2, 3]  # 상, 하, 좌, 우
ACTIONS = [(-1, 0), (1, 0), (0, -1), (0, 1)]  # 좌표로 나타낸 행동
REWARDS = []
 
 
class GraphicDisplay(tk.Tk):
    def __init__(self, value_iteration):
        super(GraphicDisplay, self).__init__()
        self.title('Value Iteration')
        self.geometry('{0}x{1}'.format(HEIGHT * UNIT, HEIGHT * UNIT + 50))
        self.texts = []
        self.arrows = []
        self.env = Env()
        self.agent = value_iteration
        self.iteration_count = 0
        self.improvement_count = 0
        self.is_moving = 0
        (self.up, self.down, self.left,
         self.right), self.shapes = self.load_images()
        self.canvas = self._build_canvas()
        self.text_reward(2, 2, "R : 1.0")
        self.text_reward(1, 2, "R : -1.0")
        self.text_reward(2, 1, "R : -1.0")
 
    def _build_canvas(self):
        canvas = tk.Canvas(self, bg='white',
                           height=HEIGHT * UNIT,
                           width=WIDTH * UNIT)
        # 버튼 초기화
        iteration_button = tk.Button(self, text="Calculate",
                                     command=self.calculate_value)
        iteration_button.configure(width=10, activebackground="#33B5E5")
        canvas.create_window(WIDTH * UNIT * 0.13, (HEIGHT * UNIT) + 10,
                             window=iteration_button)
 
        policy_button = tk.Button(self, text="Print Policy",
                                  command=self.print_optimal_policy)
        policy_button.configure(width=10, activebackground="#33B5E5")
        canvas.create_window(WIDTH * UNIT * 0.37, (HEIGHT * UNIT) + 10,
                             window=policy_button)
 
        policy_button = tk.Button(self, text="Move",
                                  command=self.move_by_policy)
        policy_button.configure(width=10, activebackground="#33B5E5")
        canvas.create_window(WIDTH * UNIT * 0.62, (HEIGHT * UNIT) + 10,
                             window=policy_button)
 
        policy_button = tk.Button(self, text="Clear", command=self.clear)
        policy_button.configure(width=10, activebackground="#33B5E5")
        canvas.create_window(WIDTH * UNIT * 0.87, (HEIGHT * UNIT) + 10,
                             window=policy_button)
 
        # 그리드 생성
        for col in range(0, WIDTH * UNIT, UNIT):  # 0~400 by 80
            x0, y0, x1, y1 = col, 0, col, HEIGHT * UNIT
            canvas.create_line(x0, y0, x1, y1)
        for row in range(0, HEIGHT * UNIT, UNIT):  # 0~400 by 80
            x0, y0, x1, y1 = 0, row, HEIGHT * UNIT, row
            canvas.create_line(x0, y0, x1, y1)
 
        # 캔버스에 이미지 추가
        self.rectangle = canvas.create_image(50, 50, image=self.shapes[0])
        canvas.create_image(250, 150, image=self.shapes[1])
        canvas.create_image(150, 250, image=self.shapes[1])
        canvas.create_image(250, 250, image=self.shapes[2])
 
        canvas.pack()
 
        return canvas
 
    def load_images(self):
        PhotoImage = ImageTk.PhotoImage
        up = PhotoImage(Image.open("../img/up.png").resize((13, 13)))
        right = PhotoImage(Image.open("../img/right.png").resize((13, 13)))
        left = PhotoImage(Image.open("../img/left.png").resize((13, 13)))
        down = PhotoImage(Image.open("../img/down.png").resize((13, 13)))
        rectangle = PhotoImage(
            Image.open("../img/rectangle.png").resize((65, 65)))
        triangle = PhotoImage(
            Image.open("../img/triangle.png").resize((65, 65)))
        circle = PhotoImage(Image.open("../img/circle.png").resize((65, 65)))
        return (up, down, left, right), (rectangle, triangle, circle)
 
    def clear(self):
 
        if self.is_moving == 0:
            self.iteration_count = 0
            self.improvement_count = 0
            for i in self.texts:
                self.canvas.delete(i)
 
            for i in self.arrows:
                self.canvas.delete(i)
 
            self.agent.value_table = [[0.0] * WIDTH for _ in range(HEIGHT)]
 
            x, y = self.canvas.coords(self.rectangle)
            self.canvas.move(self.rectangle, UNIT / 2 - x, UNIT / 2 - y)
 
    def reset(self):
        self.update()
        time.sleep(0.5)
        self.canvas.delete(self.rectangle)
        return self.canvas.coords(self.rectangle)
 
    def text_value(self, row, col, contents, font='Helvetica', size=12,
                   style='normal', anchor="nw"):
        origin_x, origin_y = 85, 70
        x, y = origin_y + (UNIT * col), origin_x + (UNIT * row)
        font = (font, str(size), style)
        text = self.canvas.create_text(x, y, fill="black", text=contents,
                                       font=font, anchor=anchor)
        return self.texts.append(text)
 
    def text_reward(self, row, col, contents, font='Helvetica', size=12,
                    style='normal', anchor="nw"):
        origin_x, origin_y = 5, 5
        x, y = origin_y + (UNIT * col), origin_x + (UNIT * row)
        font = (font, str(size), style)
        text = self.canvas.create_text(x, y, fill="black", text=contents,
                                       font=font, anchor=anchor)
        return self.texts.append(text)
 
    def rectangle_move(self, action):
        base_action = np.array([0, 0])
        location = self.find_rectangle()
        self.render()
        if action == 0 and location[0] > 0:  # up
            base_action[1] -= UNIT
        elif action == 1 and location[0] < HEIGHT - 1:  # down
            base_action[1] += UNIT
        elif action == 2 and location[1] > 0:  # left
            base_action[0] -= UNIT
        elif action == 3 and location[1] < WIDTH - 1:  # right
            base_action[0] += UNIT
 
        self.canvas.move(self.rectangle, base_action[0],
                         base_action[1])  # move agent
 
    def find_rectangle(self):
        temp = self.canvas.coords(self.rectangle)
        x = (temp[0] / 100) - 0.5
        y = (temp[1] / 100) - 0.5
        return int(y), int(x)
 
    def move_by_policy(self):
 
        if self.improvement_count != 0 and self.is_moving != 1:
            self.is_moving = 1
            x, y = self.canvas.coords(self.rectangle)
            self.canvas.move(self.rectangle, UNIT / 2 - x, UNIT / 2 - y)
 
            x, y = self.find_rectangle()
            while len(self.agent.get_action([x, y])) != 0:
                action = random.sample(self.agent.get_action([x, y]), 1)[0]
                self.after(100, self.rectangle_move(action))
                x, y = self.find_rectangle()
            self.is_moving = 0
 
    def draw_one_arrow(self, col, row, action):
        if col == 2 and row == 2:
            return
        if action == 0:  # up
            origin_x, origin_y = 50 + (UNIT * row), 10 + (UNIT * col)
            self.arrows.append(self.canvas.create_image(origin_x, origin_y,
                                                        image=self.up))
        elif action == 1:  # down
            origin_x, origin_y = 50 + (UNIT * row), 90 + (UNIT * col)
            self.arrows.append(self.canvas.create_image(origin_x, origin_y,
                                                        image=self.down))
        elif action == 3:  # right
            origin_x, origin_y = 90 + (UNIT * row), 50 + (UNIT * col)
            self.arrows.append(self.canvas.create_image(origin_x, origin_y,
                                                        image=self.right))
        elif action == 2:  # left
            origin_x, origin_y = 10 + (UNIT * row), 50 + (UNIT * col)
            self.arrows.append(self.canvas.create_image(origin_x, origin_y,
                                                        image=self.left))
 
    def draw_from_values(self, state, action_list):
        i = state[0]
        j = state[1]
        for action in action_list:
            self.draw_one_arrow(i, j, action)
 
    def print_values(self, values):
        for i in range(WIDTH):
            for j in range(HEIGHT):
                self.text_value(i, j, round(values[i][j], 2))
 
    def render(self):
        time.sleep(0.1)
        self.canvas.tag_raise(self.rectangle)
        self.update()
 
    def calculate_value(self):
        self.iteration_count += 1
        for i in self.texts:
            self.canvas.delete(i)
        self.agent.value_iteration()
        self.print_values(self.agent.value_table)
 
    def print_optimal_policy(self):
        self.improvement_count += 1
        for i in self.arrows:
            self.canvas.delete(i)
        for state in self.env.get_all_states():
            action = self.agent.get_action(state)
            self.draw_from_values(state, action)
 
 
class Env:
    def __init__(self):
        self.transition_probability = TRANSITION_PROB
        self.width = WIDTH  # Width of Grid World
        self.height = HEIGHT  # Height of GridWorld
        self.reward = [[0] * WIDTH for _ in range(HEIGHT)]
        self.possible_actions = POSSIBLE_ACTIONS
        self.reward[2][2] = 1  # reward 1 for circle
        self.reward[1][2] = -1  # reward -1 for triangle
        self.reward[2][1] = -1  # reward -1 for triangle
        self.all_state = []
 
        for x in range(WIDTH):
            for y in range(HEIGHT):
                state = [x, y]
                self.all_state.append(state)
 
    def get_reward(self, state, action):
        next_state = self.state_after_action(state, action)
        return self.reward[next_state[0]][next_state[1]]
 
    def state_after_action(self, state, action_index):
        action = ACTIONS[action_index]
        return self.check_boundary([state[0] + action[0], state[1] + action[1]])
 
    @staticmethod
    def check_boundary(state):
        state[0] = (0 if state[0] < 0 else WIDTH - 1
        if state[0] > WIDTH - 1 else state[0])
        state[1] = (0 if state[1] < 0 else HEIGHT - 1
        if state[1] > HEIGHT - 1 else state[1])
        return state
 
    def get_transition_prob(self, state, action):
        return self.transition_probability
 
    def get_all_states(self):
        return self.all_state
 

 

value_iteration.py

import numpy as np
from environment import GraphicDisplay, Env
 
 
class ValueIteration:
    def __init__(self, env):
        # 환경에 대한 객체 선언
        self.env = env
        # 가치 함수를 2차원 리스트로 초기화
        self.value_table = [[0.0] * env.width for _ in range(env.height)]
        # 할인율
        self.discount_factor = 0.9
 
    # 벨만 최적 방정식을 통해 다음 가치 함수 계산
    def value_iteration(self):
        # 다음 가치함수 초기화
        next_value_table = [[0.0] * self.env.width 
                           for _ in range(self.env.height)]
 
        # 모든 상태에 대해서 벨만 최적방정식을 계산                           
        for state in self.env.get_all_states():
            # 마침 상태의 가치 함수 = 0
            if state == [2, 2]:
                next_value_table[state[0]][state[1]] = 0.0
                continue
 
            # 벨만 최적 방정식
            value_list = []
            for action in self.env.possible_actions:
                next_state = self.env.state_after_action(state, action)
                reward = self.env.get_reward(state, action)
                next_value = self.get_value(next_state)
                value_list.append((reward + self.discount_factor * next_value))
 
            # 최댓값을 다음 가치 함수로 대입 ( 기대방정식과 다른 점 )
            next_value_table[state[0]][state[1]] = max(value_list)
 
        self.value_table = next_value_table
 
    # 현재 가치 함수로부터 행동을 반환
    def get_action(self, state):
        if state == [2, 2]:
            return []
 
        # 모든 행동에 대해 (보상 + (감가율 * 다음 상태 가치함수)) = 큐함수 를 계산
        value_list = []
        for action in self.env.possible_actions:
            next_state = self.env.state_after_action(state, action)
            reward = self.env.get_reward(state, action)
            next_value = self.get_value(next_state)
            value = (reward + self.discount_factor * next_value)
            value_list.append(value)
 
        # 최대 큐 함수를 가진 행동(복수일 경우 여러 개)을 반환
        max_idx_list = np.argwhere(value_list == np.amax(value_list))
        action_list = max_idx_list.flatten().tolist()
        return action_list # 정책이터레이션과 다르게 정책을 반환하지않음. 최적방정식의 경우는 정책이 밖으로 드러나지 않으므로 ! 오직 가치함수 최댓값만으로 행동을 결정
 
    def get_value(self, state):
        return self.value_table[state[0]][state[1]]
 
 
if __name__ == "__main__":
    env = Env()
    value_iteration = ValueIteration(env)
    grid_world = GraphicDisplay(value_iteration)
    grid_world.mainloop()