agent.py

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
MAX_MEMORY = 100_000
BATCH_SIZE = 1000
LR = 0.001

class Agent:


    def __init__(self):
        self.n_games = 0
        self.epsilon = 0 #radnness
        self.gamma = 0.9 # discount rate
        self.memory = deque(maxlen=MAX_MEMORY)
        self.model = Linear_QNet(11,256,3)
        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 = [
            #Danger straight
            (dir_r and game.is_collision(point_r)) or
            (dir_r and game.is_collision(point_l)) or
            (dir_r and game.is_collision(point_u)) or
            (dir_r and game.is_collision(point_d)),

            #Danger right
            (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)),

            #Danger left
            (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)),

            #Move direction
            dir_l,
            dir_r,
            dir_u,
            dir_d,

            #food location
            game.food.x < game.head.x, #food left
            game.food.x > game.head.x, #food right
            game.food.y < game.head.y, #food up
            game.food.y > game.head.y #food down
        ]

        return np.array(state, dtype=int)
    
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((self, state, action, reward, next_state, done)) #pop left

    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(self, states, actions, rewards, next_states, dones)   
    def train_short_memory(self, state, action, reward, next_state, done):
        self.trainer.train_step(self, state, action, reward, next_state, done)

    def get_action(self, state):
        #random moves: tradeoff exploration/exploitation
        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()
        game = SnakeGameAI()
        while True:
            #get old state
            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)

            #train short memory
            agent.train_short_memory(state_old, final_move, reward, state_new,done)

            #remember
            agent.remember(state_old, final_move, reward, state_new,done)
                
            if done:
                #train long memory (replay memory) film review!
                game.reset()
                agent.n_games +=1
                agent.train_long_memory()

                if score > record:
                    record = score
                    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()