rish-16

# Crossing over the Primary Agents
def crossover_pagent(agents):
	offspring = []
        for _ in range(int((self.population - len(agents)) / 2)):
            parent1 = random.choice(agents)
            parent2 = random.choice(agents)
            child1 = PAgent(self.in_str_len)
            child2 = PAgent(self.in_str_len)
            split = random.randint(0, self.in_str_len)
            child1.string = parent1.string[0:split] + parent2.string[split:self.in_str_len]

rish-16

'use strict';

// Requiring the Dynamoose NPM package
var dynamoose = require('dynamoose');

// To configure Dynamose you can either:
/*
Set environment variables

export AWS_ACCESS_KEY_ID="Your AWS Access Key ID"

rish-16

'use strict';

// Requiring the Dynamoose NPM package
var dynamoose = require('dynamoose');

// Setting our table name prefix to "example-"
dynamoose.setDefaults({
	prefix: 'example-',
	suffix: ''
});

rish-16

backend: TkAgg

rish-16

from PIL import Image
import numpy as np

gray_images_train = []

# `train_images` is an array containing the image file paths
for img in train_images:
	img = Image.open(img)
	gray = img.convert('L')
	gray_images_train.append(gray)

rish-16

import cirq

# Build a model using Circuits
circuit = cirq.Circuit()

# Create Qubits
(q0, q1) = cirq.LineQubit.range(2)

# Add Qubits to Circuit model using logic gates
circuit.append([cirq.H(q0), cirq.CNOT(q0, q1)])

rish-16

import pandas as pd
import numpy
import random

actions = ['up', 'down', 'left', 'right']
states = ['start', 'mousetrap', 'empty', 'cheese', 'end']
n_actions = len(actions)
n_states = len(states)

q_table = pd.DataFrame(np.zeros([n_states, n_actions]), columns=actions)

rish-16

discount_factor_gamma = 0.9
num_episodes = 1000
learning_rate_alpha = 0.8
epsilon_threshold = 1.0

rish-16

rewards_lookup = {
	'empty': -1,
	'cheese': 5,
	'mousetrap': -10,
	'start': 0,
	'end': 0
}

rish-16

# The main RL event loop
for i in range(num_episodes):
    S0 = env.reset() # Current state
    total_reward_for_episode = 0
    game_done = False

    for s in range(steps_per_episode):
        # Choosing an action to perform in the current state
        if np.random.rand() > epsilon:
            action = env.action_space.sample() # Explore with a random action