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Snake_AI.py

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+import torch
+import random
+import numpy as np
+from collections import deque
+from game import SnakeGameAI, Direction, Point
+from model import Linear_QNet, QTrainer
+from helper import plot
+import joblib
+import os
+from pathlib import Path
+
+MAX_MEMORY = 100_000
+BATCH_SIZE = 1000
+LR = 0.001
+
+class Agent:
+
+    def __init__(self):
+        self.n_games = 0
+        self.epsilon = 0 
+        self.gamma = 0.9 
+        self.memory = deque(maxlen=MAX_MEMORY) #
+        self.model = Linear_QNet(11, 512, 3)
+        self.trainer = QTrainer(self.model, lr=LR, gamma=self.gamma)
+
+
+    def get_state(self, game):
+        head = game.snake[0]
+        point_l = Point(head.x - 20, head.y)
+        point_r = Point(head.x + 20, head.y)
+        point_u = Point(head.x, head.y - 20)
+        point_d = Point(head.x, head.y + 20)
+
+        dir_l = game.direction == Direction.LEFT
+        dir_r = game.direction == Direction.RIGHT
+        dir_u = game.direction == Direction.UP
+        dir_d = game.direction == Direction.DOWN
+
+        state = [
+
+            (dir_r and game.is_collision(point_r)) or 
+            (dir_l and game.is_collision(point_l)) or 
+            (dir_u and game.is_collision(point_u)) or 
+            (dir_d and game.is_collision(point_d)),
+
+            (dir_u and game.is_collision(point_r)) or 
+            (dir_d and game.is_collision(point_l)) or 
+            (dir_l and game.is_collision(point_u)) or 
+            (dir_r and game.is_collision(point_d)),
+
+            (dir_d and game.is_collision(point_r)) or 
+            (dir_u and game.is_collision(point_l)) or 
+            (dir_r and game.is_collision(point_u)) or 
+            (dir_l and game.is_collision(point_d)),
+
+            dir_l,
+            dir_r,
+            dir_u,
+            dir_d,
+
+            game.food.x < game.head.x,
+            game.food.x > game.head.x, 
+            game.food.y < game.head.y, 
+            game.food.y > game.head.y 
+            ]
+
+        return np.array(state, dtype=int)
+
+    def remember(self, state, action, reward, next_state, done):
+        self.memory.append((state, action, reward, next_state, done)) 
+
+    def train_long_memory(self):
+        if len(self.memory) > BATCH_SIZE:
+            mini_sample = random.sample(self.memory, BATCH_SIZE) 
+        else:
+            mini_sample = self.memory
+        states, actions, rewards, next_states, dones = zip(*mini_sample)
+        self.trainer.train_step(states, actions, rewards, next_states, dones)
+
+    def train_short_memory(self, state, action, reward, next_state, done):
+        self.trainer.train_step(state, action, reward, next_state, done)
+
+    def get_action(self, state):
+        self.epsilon = 80 - self.n_games
+        final_move = [0,0,0]
+        if random.randint(0, 200) < self.epsilon:
+            move = random.randint(0, 2)
+            final_move[move] = 1
+        else:
+            state0 = torch.tensor(state, dtype=torch.float)
+            prediction = self.model(state0)
+            move = torch.argmax(prediction).item()
+            final_move[move] = 1
+
+        return final_move
+
+
+def train():
+
+    plot_scores = []
+    plot_mean_scores = []
+    total_score = 0
+    record = 0
+    agent = Agent()
+    ans = int(input("Train or Load Model [0,1]"))
+
+    while ans not in [0,1]:
+        print("invalid input")
+        ans = int(input("Train or Load Model [0,1]"))
+
+    if ans == 1:
+        agent = joblib.load((os.fspath(Path(__file__).resolve().parent / "best.pkl")))
+
+    game = SnakeGameAI() 
+
+    while True:
+        if game.save == True:
+            joblib.dump(agent, (os.fspath(Path(__file__).resolve().parent / "model.pkl")))
+        state_old = agent.get_state(game)
+        final_move = agent.get_action(state_old)
+        reward, done, score = game.play_step(final_move)
+        state_new = agent.get_state(game)
+        agent.train_short_memory(state_old, final_move, reward, state_new, done)
+        agent.remember(state_old, final_move, reward, state_new, done)
+
+        if done:
+            game.reset()
+            agent.n_games += 1
+            agent.train_long_memory()
+
+            if score > record:
+                record = score
+                joblib.dump(agent,(os.fspath(Path(__file__).resolve().parent / "model.pkl")))
+                agent.model.save()
+
+            print('Game', agent.n_games, 'Score', score, 'Record:', record)
+
+            plot_scores.append(score)
+            total_score += score
+            mean_score = total_score / agent.n_games
+            plot_mean_scores.append(mean_score)
+            plot(plot_scores, plot_mean_scores)
+
+
+if __name__ == '__main__':
+    train()
+

game.py

@@ -0,0 +1,141 @@
+import pygame
+import random
+from enum import Enum
+from collections import namedtuple
+import numpy as np
+
+
+pygame.init()
+
+font = pygame.font.SysFont('calibri', 20)
+
+class Direction(Enum):
+    RIGHT = 1
+    LEFT = 2
+    UP = 3
+    DOWN = 4
+
+Point = namedtuple('Point', 'x, y')
+
+WHITE = (255, 255, 255)
+RED = (248,143,147)
+GREEN = (186,217,181)
+FONT = (66,12,20)
+BACK = (239,247,207)
+
+BLOCK_SIZE = 20
+SPEED = 20
+
+class SnakeGameAI:
+
+    def __init__(self, w=640, h=480):
+        self.w = w
+        self.h = h
+        self.display = pygame.display.set_mode((self.w, self.h))
+        pygame.display.set_caption('Snake')
+        self.clock = pygame.time.Clock()
+        self.reset()
+        self.save = False
+
+    def reset(self):
+        self.direction = Direction.RIGHT
+
+        self.head = Point(self.w/2, self.h/2)
+        self.snake = [self.head, 
+                      Point(self.head.x-BLOCK_SIZE, self.head.y),
+                      Point(self.head.x-(2*BLOCK_SIZE), self.head.y)]
+
+        self.score = 0
+        self.food = None
+        self._place_food()
+        self.loop_count = 0
+
+    def _place_food(self):
+        x = random.randint(0, (self.w-BLOCK_SIZE )//BLOCK_SIZE )*BLOCK_SIZE 
+        y = random.randint(0, (self.h-BLOCK_SIZE )//BLOCK_SIZE )*BLOCK_SIZE
+        self.food = Point(x, y)
+        if self.food in self.snake:
+            self._place_food()
+
+    def play_step(self, action):
+        self.loop_count += 1
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                pygame.quit()
+                quit()
+        keys_pressed = pygame.key.get_pressed()
+
+        if keys_pressed[pygame.K_u]:
+            print("saving")
+            self.save = True
+        self._move(action) 
+        self.snake.insert(0, self.head)
+        reward = 0
+        game_over = False
+        if self.is_collision() or self.loop_count > 100*len(self.snake):
+            reward = -10
+            game_over = True
+            return reward, game_over, self.score
+
+        if self.head == self.food:
+            self.score += 1
+            reward = 10
+            self._place_food()
+        else:
+            self.snake.pop()
+
+        self._update_ui()
+        self.clock.tick(SPEED)
+        return reward,game_over, self.score
+
+    def is_collision(self, pt=None):
+        if pt is None:
+            pt = self.head
+
+        if pt.x > self.w - BLOCK_SIZE or pt.x < 0 or pt.y > self.h - BLOCK_SIZE or pt.y < 0:
+            return True
+        if pt in self.snake[1:]:
+            return True
+
+        return False
+
+    def _update_ui(self):
+        self.display.fill(BACK)
+
+        for pt in self.snake:
+            pygame.draw.rect(self.display, GREEN, pygame.Rect(pt.x, pt.y, BLOCK_SIZE, BLOCK_SIZE))
+
+        pygame.draw.rect(self.display, RED, pygame.Rect(self.food.x, self.food.y, BLOCK_SIZE, BLOCK_SIZE))
+
+        text = font.render( str(self.score), True, GREEN)
+        self.display.blit(text, [320, 5])
+        pygame.display.flip()
+
+    def _move(self, action):
+        clock_wise = [Direction.RIGHT, Direction.DOWN,Direction.LEFT,Direction.UP]
+        idx = clock_wise.index(self.direction)
+
+        if np.array_equal(action, [1,0,0]):
+            new_dir = clock_wise[idx]
+
+        elif np.array_equal(action, [0,1,0]):
+            next_idx = (idx+1) % 4
+            new_dir = clock_wise[next_idx]
+        else:
+            next_idx = (idx-1) % 4
+            new_dir = clock_wise[next_idx]
+        self.direction = new_dir
+
+        x = self.head.x
+        y = self.head.y
+        if self.direction == Direction.RIGHT:
+            x += BLOCK_SIZE
+        elif self.direction == Direction.LEFT:
+            x -= BLOCK_SIZE
+        elif self.direction == Direction.DOWN:
+            y += BLOCK_SIZE
+        elif self.direction == Direction.UP:
+            y -= BLOCK_SIZE
+
+        self.head = Point(x, y)
+

helper.py

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+import matplotlib.pyplot as plt
+from IPython import display
+
+plt.ion()
+
+def plot(scores, mean_scores):
+    display.clear_output(wait=True)
+    display.display(plt.gcf())
+    plt.clf()
+    plt.style.use('ggplot')
+    plt.title('Training...')
+    plt.xlabel('Number of Games')
+    plt.ylabel('Score')
+    plt.plot(scores)
+    plt.plot(mean_scores,linestyle=":")
+    plt.ylim(ymin=0)
+    plt.text(len(scores)-1, scores[-1], str(scores[-1]))
+    plt.text(len(mean_scores)-1, mean_scores[-1], str(mean_scores[-1]))
+    plt.show(block=False)
+    plt.pause(.1)

model.py

@@ -0,0 +1,65 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import os
+from pathlib import Path
+
+class Linear_QNet(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super().__init__()
+        self.linear1 = nn.Linear(input_size, hidden_size)
+        self.linear2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = F.relu(self.linear1(x))
+        x = self.linear2(x)
+        return x
+
+    def save(self, file_name='model.pth'):
+        torch.save(self.state_dict(), (os.fspath(Path(__file__).resolve().parent / file_name)))
+
+    def load(self):
+        self = torch.load((os.fspath(Path(__file__).resolve().parent / 'model.pth')))
+
+
+class QTrainer:
+    def __init__(self, model, lr, gamma):
+        self.lr = lr
+        self.gamma = gamma
+        self.model = model
+        self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
+        self.criterion = nn.MSELoss()
+
+    def train_step(self, state, action, reward, next_state, done):
+        state = torch.tensor(state, dtype=torch.float)
+        next_state = torch.tensor(next_state, dtype=torch.float)
+        action = torch.tensor(action, dtype=torch.long)
+        reward = torch.tensor(reward, dtype=torch.float)
+
+        if len(state.shape) == 1:
+            state = torch.unsqueeze(state, 0)
+            next_state = torch.unsqueeze(next_state, 0)
+            action = torch.unsqueeze(action, 0)
+            reward = torch.unsqueeze(reward, 0)
+            done = (done, )
+        pred = self.model(state)
+
+        target = pred.clone()
+        for idx in range(len(done)):
+            Q_new = reward[idx]
+            if not done[idx]:
+                Q_new = reward[idx] + self.gamma * torch.max(self.model(next_state[idx]))
+
+            target[idx][torch.argmax(action[idx]).item()] = Q_new
+
+        self.optimizer.zero_grad()
+        loss = self.criterion(target, pred)
+        loss.backward()
+
+        self.optimizer.step()        
+
+
+
+
+