ai.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 11 07:38:45 2019

@author: tejas
"""
import random
import numpy as np
from collections import deque
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout
from tensorflow.python.keras.optimizers import Adam

import logging
logger = logging.getLogger("mario")
        

class AI:

    def __init__(self, action_size, input_shape, batch_size = 32):
        
        logger.info("Starting AI")
        self.action_size = action_size
        self.input_shape = input_shape
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95    # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
        self.batch_size = batch_size
        self.count = 0

    def _build_model(self):
        # Neural Net for Deep-Q learning Model
        logger.info("Building Model")
        model = Sequential()
        #model.add(Conv2D(filters = 128, kernel_size = (3,3), activation = "relu", input_shape = self.input_shape))
        #model.add(MaxPooling2D(pool_size=(2, 2)))
        #model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = "relu"))
        #model.add(MaxPooling2D(pool_size=(2, 2)))
        #model.add(Dropout(0.25))

        #model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = "relu"))
        #model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = "relu"))
        #model.add(MaxPooling2D(pool_size=(2, 2)))
        #model.add(Dropout(0.25))

        #model.add(Flatten())
        model.add(Dense(units = 32, activation='relu',input_shape = self.input_shape))
        model.add(Dropout(0.2))
        model.add(Dense(units = 32, activation='relu'))
        model.add(Dropout(0.2))
        model.add(Dense(12, activation='relu'))
        model.add(Dense(self.action_size, activation='sigmoid'))
        model.compile(loss='mse',
                      optimizer=Adam(lr=self.learning_rate))
        logging.debug("Built Network")
        logging.debug(model.summary())
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def action(self, state):
        return np.argmax(self.model.predict(state))
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])  # returns action

    def replay(self):
        logger.debug("Training Model")
        self.count = self.count + 1  
        minibatch = random.sample(self.memory, self.batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
            if self.count % 5 == 0:
                logger.info("Espilon Decay :- {}".format(self.epsilon))

    def load(self, name):
        logger.info("Loading Model")
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)
    
    def getModel(self):
        return self.model