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import 'dart:async'; | |
import 'dart:math' as math; | |
import 'package:flutter/material.dart'; | |
class SwipeCard extends StatefulWidget { | |
final Widget child; | |
final void Function(DragStartDetails details)? onSwipeStart; |
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board_size = 4 | |
output_size = 4 | |
learning_rate = 1e-3 | |
maximum_discount = .99 | |
random_action_threshold = 0.1 | |
game_max_length = 1024 | |
num_episodes = 8000 | |
save_interval = 100 | |
env = game.Game(board_size) | |
rlModel = model.RLModel(learning_rate=learning_rate) |
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... | |
def distance_to_apple(self, pos): | |
return ((pos[0] - self.apple[0]) ** 2 + (pos[1] - self.apple[1]) ** 2) ** (1 / 2) | |
def get_reward(self): | |
distance = self.distance_to_apple(self.snake[0]) | |
last_distance = self.distance_to_apple(self.snake[1]) | |
if last_distance > distance: | |
return 0 | |
return -1 |
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def train_single_step(self, state0, state1, a, reward, maximum_discount): | |
Q0 = self.predict(state0) | |
Q1 = np.argmax(self.predict(state1)[0]) | |
Q0[0][a] = reward + maximum_discount * Q1 | |
self.model.fit(np.array(state0).reshape(1, -1), Q0, epochs=1, verbose=0) |
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import tensorflow as tf | |
import pickle | |
import numpy as np | |
class RLModel: | |
def __init__(self, version = None, learning_rate = 1e-3): | |
if version is not None: | |
self.retrieveVariables(version) | |
else: | |
self.learning_rate = learning_rate |
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import pygame | |
from pygame.locals import QUIT, KEYDOWN, K_ESCAPE, K_UP, K_DOWN, K_LEFT, K_RIGHT, K_r | |
import game | |
import model | |
import numpy as np | |
version = 'newModel' | |
board_size = 4 | |
screen_size = 512 | |
block_size = screen_size / board_size |
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import random | |
import pygame | |
screen_size = 512 | |
background = None | |
class Game: | |
def __init__(self, board_size): | |
self.board_size = board_size | |
self.clear_board() |
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plt.plot(rList) | |
plt.plot(jList) |
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j+=1 | |
s = env.get_board() | |
a = np.argmax(rlModel.predict(s)[0]) | |
if np.random.rand(1) < random_action_threshold: | |
a = env.random_action() | |
s1, reward, done = env.step(a) | |
rlModel.train_single_step(s, s1, a, reward, maximum_discount) | |
rAll += reward | |
if done: | |
break |
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jList = [] | |
rList = [] | |
for i in range(num_episodes): | |
s = env.clear_board() | |
rAll = 0 | |
d = False | |
j = 0 | |
while j < game_max_length: | |
# Game step... | |
if i % save_interval == 0 and i > 0: |
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