Juna Salviati antigones

## main.py
from wolf_goat_cabbage_qlearning import WolfGoatCabbageQLearning


def main():
    wcg_arena = WolfGoatCabbageQLearning(
        start_state=[['🥦', '🐐', '⛵', '🐺'], [], []],
        goal_state=[[], [], ['⛵', '🐐', '🥦', '🐺']],
        gamma=0.8,
        max_episodes=1000,
        epsilon_greedy=True)

## wolf_goat_cabbage_qlearning.py
class RLKey:
    def __init__(self, k1, k2):
        self.k1 = k1
        self.k2 = k2

    def __hash__(self):
        return hash((tuple(frozenset(x) for x in self.k1),
                tuple(frozenset(x) for x in self.k2)))

    def __eq__(self, other):

## wolf_goat_cabbage_qlearning.py
    def train(self):
        convergence_count = 0
        q_s_a = defaultdict(int)
        q_s_a_prec = copy.deepcopy(q_s_a)
        episode = 1
        scores = []
        eps_list = []
        rewards = {}

        for episode in range(1,self.max_episodes):

## wolf_goat_cabbage_qlearning.py
    def move(self, state, what, where_from, where_to):
        state[where_from].remove(what)
        state[where_to].add(what)

    def get_next_state(self, starting_state, action):

        next_state = copy.deepcopy(starting_state)

        if action == 'MOVE_PLAYER_FROM_LEFT_TO_BOAT':
            if '⛵' in next_state[0]:

## wolf_goat_cabbage_qlearning.py
class WolfGoatCabbageQLearning:

    def __init__(self, start_state, goal_state, gamma=0.8, max_episodes=50000,
                 epsilon_greedy=True, min_epsilon=0.1, max_epsilon=1.0):
        self.start_state = tuple(set(x) for x in start_state)
        self.goal_state = tuple(set(x) for x in goal_state)

        self.gamma = gamma
        self.max_episodes = max_episodes

## wolf_goat_cabbage_qlearning.py
    def get_reward(self, state):
        # GOAT and WOLF cannot be left unsupervised together
        # GOAT and CABBAGE cannot be left unsupervised together
        if state[2] == self.goal_state[2]:
            return 100
        if {'🐐', '🐺'} <= state[0] and '⛵' not in state[0]:
            return -100
        if {'🐐', '🥦'} <= state[0] and '⛵' not in state[0]:
            return -100
        if {'🐐', '🐺'} <= state[2] and '⛵' not in state[2]:

## 12_days_of_xmas.py
"""
    Have yourself a merry coding Christmas...
"""
from num2words import num2words


def sing():
    things_my_true_love_gave_to_me = [
        (1, 'partridge in a pear tree'),
        (2, 'turtle doves'),

## index.html
Leap.loop(controllerOptions, function(frame) {
		// Body of callback function
		var result = "";
		var extendedFingers = 0;
		if (frame.hands.length > 0) {
			var hand = frame.hands[0];
			for(var f = 0; f < hand.fingers.length; f++){
				var finger = hand.fingers[f];
				if (finger.extended) extendedFingers++;
			}

## gist:4890b4622c421f66c095a2829065bcf3
<html>
  <head>
    <title>Leap Test</title>

     <style type="text/css">
       #out {
	  font-size: 60px;
	}
     </style>


## MainPage.xaml.cs
using System;
using Xamarin.Forms;

namespace MagicBall
{
    public partial class MainPage : ContentPage
    {
        readonly Prediction prediction = new Prediction();
        public MainPage()
        {
	from wolf_goat_cabbage_qlearning import WolfGoatCabbageQLearning


	def main():
	wcg_arena = WolfGoatCabbageQLearning(
	start_state=[['🥦', '🐐', '⛵', '🐺'], [], []],
	goal_state=[[], [], ['⛵', '🐐', '🥦', '🐺']],
	gamma=0.8,
	max_episodes=1000,
	epsilon_greedy=True)
	class RLKey:
	def __init__(self, k1, k2):
	self.k1 = k1
	self.k2 = k2

	def __hash__(self):
	return hash((tuple(frozenset(x) for x in self.k1),
	tuple(frozenset(x) for x in self.k2)))

	def __eq__(self, other):
	def train(self):
	convergence_count = 0
	q_s_a = defaultdict(int)
	q_s_a_prec = copy.deepcopy(q_s_a)
	episode = 1
	scores = []
	eps_list = []
	rewards = {}

	for episode in range(1,self.max_episodes):
	def move(self, state, what, where_from, where_to):
	state[where_from].remove(what)
	state[where_to].add(what)

	def get_next_state(self, starting_state, action):

	next_state = copy.deepcopy(starting_state)

	if action == 'MOVE_PLAYER_FROM_LEFT_TO_BOAT':
	if '⛵' in next_state[0]:
	class WolfGoatCabbageQLearning:

	def __init__(self, start_state, goal_state, gamma=0.8, max_episodes=50000,
	epsilon_greedy=True, min_epsilon=0.1, max_epsilon=1.0):
	self.start_state = tuple(set(x) for x in start_state)
	self.goal_state = tuple(set(x) for x in goal_state)

	self.gamma = gamma
	self.max_episodes = max_episodes
	def get_reward(self, state):
	# GOAT and WOLF cannot be left unsupervised together
	# GOAT and CABBAGE cannot be left unsupervised together
	if state[2] == self.goal_state[2]:
	return 100
	if {'🐐', '🐺'} <= state[0] and '⛵' not in state[0]:
	return -100
	if {'🐐', '🥦'} <= state[0] and '⛵' not in state[0]:
	return -100
	if {'🐐', '🐺'} <= state[2] and '⛵' not in state[2]:
	"""
	Have yourself a merry coding Christmas...
	"""
	from num2words import num2words


	def sing():
	things_my_true_love_gave_to_me = [
	(1, 'partridge in a pear tree'),
	(2, 'turtle doves'),
	Leap.loop(controllerOptions, function(frame) {
	// Body of callback function
	var result = "";
	var extendedFingers = 0;
	if (frame.hands.length > 0) {
	var hand = frame.hands[0];
	for(var f = 0; f < hand.fingers.length; f++){
	var finger = hand.fingers[f];
	if (finger.extended) extendedFingers++;
	}
	<html>
	<head>
	<title>Leap Test</title>

	<style type="text/css">
	#out {
	font-size: 60px;
	}
	</style>
	using System;
	using Xamarin.Forms;

	namespace MagicBall
	{
	public partial class MainPage : ContentPage
	{
	readonly Prediction prediction = new Prediction();
	public MainPage()
	{