Many shashed commits

.gitignore

@@ -2,3 +2,5 @@ __pycache__
 *.~*
 memoryBank
 memoryBank/*
+*.egg-info
+.venv

README.md

@@ -2,21 +2,24 @@
 
 Project vacuumDecay is a framework for building AIs for games.  
 Avaible architectures are
- - those used in Deep Blue (mini-max / expecti-max)
- - advanced expecti-max exploration based on utility heuristics
- - those used in AlphaGo Zero (knowledge distilation using neural-networks)
+
+- those used in Deep Blue (mini-max / expecti-max)
+- advanced expecti-max exploration based on utility heuristics
+- those used in AlphaGo Zero (knowledge distilation using neural-networks)
 
 A new AI is created by subclassing the State-class and defining the following functionality (mycelia.py provies a template):
- - initialization (generating the gameboard or similar)
- - getting avaible actions for the current situation (returns an Action-object, which can be subclassed to add additional functionality)
- - applying an action (the state itself should be immutable, a new state should be returned)
- - checking for a winning-condition (should return None if game has not yet ended)
- - (optional) a getter for a string-representation of the current state
- - (optional) a heuristic for the winning-condition (greatly improves capability)
- - (optional) a getter for a tensor that describes the current game state (required for knowledge distilation)
- - (optional) interface to allow a human to select an action
+
+- initialization (generating the gameboard or similar)
+- getting avaible actions for the current situation (returns an Action-object, which can be subclassed to add additional functionality)
+- applying an action (the state itself should be immutable, a new state should be returned)
+- checking for a winning-condition (should return None if game has not yet ended)
+- (optional) a getter for a string-representation of the current state
+- (optional) a heuristic for the winning-condition (greatly improves capability for expecti-max)
+- (optional) a getter for a tensor that describes the current game state (required for knowledge distilation)
+- (optional) interface to allow a human to select an action
 
 ### Current state of the project
+
 The only thing that currently works is the AI for Ultimate TicTacToe.  
 It uses a trained neural heuristic (neuristic)  
 You can train it or play against it (will also train it) using 'python ultimatetictactoe.py'

mycelia.py

@@ -1,18 +0,0 @@
-class State():
-    pass
-
-
-class Action():
-    pass
-
-
-class BotAction():
-    pass
-
-
-class PlayerAction():
-    pass
-
-
-class EnvAction():
-    pass

pyproject.toml

@@ -0,0 +1,19 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vacuumDecay"
+version = "0.1.0"
+dependencies = [
+    "torch",
+    "numpy",
+    "flask",
+    "flask-socketio",
+    "networkx",
+    "pillow"
+]
+
+[tool.setuptools]
+packages = ["vacuumDecay"]
+

vacuumDecay.py

@@ -1,669 +0,0 @@
-if __name__ == '__main__':
-    print('[!] VacuumDecay should not be started directly')
-    exit()
-
-import os
-import io
-import time
-import random
-import threading
-import torch
-import torch.nn as nn
-from torch import optim
-from math import sqrt, pow, inf
-#from multiprocessing import Event
-from abc import ABC, abstractmethod
-from threading import Event
-from queue import PriorityQueue, Empty
-from dataclasses import dataclass, field
-from typing import Any
-import random
-import datetime
-import pickle
-
-
-class Action():
-    # Should hold the data representing an action
-    # Actions are applied to a State in State.mutate
-
-    def __init__(self, player, data):
-        self.player = player
-        self.data = data
-
-    def __eq__(self, other):
-        # This should be implemented differently
-        # Two actions of different generations will never be compared
-        if type(other) != type(self):
-            return False
-        return str(self.data) == str(other.data)
-
-    def __str__(self):
-        # should return visual representation of this action
-        # should start with < and end with >
-        return "<P"+str(self.player)+"-"+str(self.data)+">"
-
-
-class State(ABC):
-    # Hold a representation of the current game-state
-    # Allows retriving avaible actions (getAvaibleActions) and applying them (mutate)
-    # Mutations return a new State and should not have any effect on the current State
-    # Allows checking itself for a win (checkWin) or scoring itself based on a simple heuristic (getScore)
-    # The calculated score should be 0 when won; higher when in a worse state; highest for loosing
-    # getPriority is used for prioritising certain Nodes / States when expanding / walking the tree
-
-    def __init__(self, curPlayer=0, generation=0, playersNum=2):
-        self.curPlayer = curPlayer
-        self.generation = generation
-        self.playersNum = playersNum
-
-    @abstractmethod
-    def mutate(self, action):
-        # Returns a new state with supplied action performed
-        # self should not be changed
-        return State(curPlayer=(self.curPlayer+1) % self.playersNum, generation=self.generation+1, playersNum=self.playersNum)
-
-    @abstractmethod
-    def getAvaibleActions(self):
-        # Should return an array of all possible actions
-        return []
-
-    def askUserForAction(self, actions):
-        return choose('What does player '+str(self.curPlayer)+' want to do?', actions)
-
-    # improveMe
-    def getPriority(self, score, cascadeMemory):
-        # Used for ordering the priority queue
-        # Priority should not change for the same root
-        # Lower prioritys get worked on first
-        # Higher generations should have higher priority
-        # Higher cascadeMemory (more influence on higher-order-scores) should have lower priority
-        return -cascadeMemory + 100
-
-    @abstractmethod
-    def checkWin(self):
-        # -1 -> Draw
-        # None -> Not ended
-        # n e N -> player n won
-        return None
-
-    # improveMe
-    def getScoreFor(self, player):
-        # 0 <= score <= 1; should return close to zero when we are winning
-        w = self.checkWin()
-        if w == None:
-            return 0.5
-        if w == player:
-            return 0
-        if w == -1:
-            return 0.9
-        return 1
-
-    @abstractmethod
-    def __str__(self):
-        # return visual rep of state
-        return "[#]"
-
-    @abstractmethod
-    def getTensor(self, player=None, phase='default'):
-        if player == None:
-            player = self.curPlayer
-        return torch.tensor([0])
-
-    @classmethod
-    def getModel(cls, phase='default'):
-        pass
-
-    def getScoreNeural(self, model, player=None, phase='default'):
-        return model(self.getTensor(player=player, phase=phase)).item()
-
-
-class Universe():
-    def __init__(self):
-        self.scoreProvider = 'naive'
-
-    def newOpen(self, node):
-        pass
-
-    def merge(self, node):
-        return node
-
-    def clearPQ(self):
-        pass
-
-    def iter(self):
-        return []
-
-    def activateEdge(self, head):
-        pass
-
-
-@dataclass(order=True)
-class PQItem:
-    priority: int
-    data: Any = field(compare=False)
-
-
-class QueueingUniverse(Universe):
-    def __init__(self):
-        super().__init__()
-        self.pq = PriorityQueue()
-
-    def newOpen(self, node):
-        item = PQItem(node.getPriority(), node)
-        self.pq.put(item)
-
-    def merge(self, node):
-        self.newOpen(node)
-        return node
-
-    def clearPQ(self):
-        self.pq = PriorityQueue()
-
-    def iter(self):
-        while True:
-            try:
-                yield self.pq.get(False).data
-            except Empty:
-                return None
-
-    def activateEdge(self, head):
-        head._activateEdge()
-
-
-class Node():
-    def __init__(self, state, universe=None, parent=None, lastAction=None):
-        self.state = state
-        if universe == None:
-            print('[!] No Universe defined. Spawning one...')
-            universe = Universe()
-        self.universe = universe
-        self.parent = parent
-        self.lastAction = lastAction
-
-        self._childs = None
-        self._scores = [None]*self.state.playersNum
-        self._strongs = [None]*self.state.playersNum
-        self._alive = True
-        self._cascadeMemory = 0  # Used for our alternative to alpha-beta pruning
-
-    def kill(self):
-        self._alive = False
-
-    def revive(self):
-        self._alive = True
-
-    @property
-    def childs(self):
-        if self._childs == None:
-            self._expand()
-        return self._childs
-
-    def _expand(self):
-        self._childs = []
-        actions = self.state.getAvaibleActions()
-        for action in actions:
-            newNode = Node(self.state.mutate(action),
-                           self.universe, self, action)
-            self._childs.append(self.universe.merge(newNode))
-
-    def getStrongFor(self, player):
-        if self._strongs[player] != None:
-            return self._strongs[player]
-        else:
-            return self.getScoreFor(player)
-
-    def _pullStrong(self):  # Currently Expecti-Max
-        strongs = [None]*self.playersNum
-        for p in range(self.playersNum):
-            cp = self.state.curPlayer
-            if cp == p:  # P owns the turn; controlls outcome
-                best = inf
-                for c in self.childs:
-                    if c.getStrongFor(p) < best:
-                        best = c.getStrongFor(p)
-                strongs[p] = best
-            else:
-                scos = [(c.getStrongFor(p), c.getStrongFor(cp))
-                        for c in self.childs]
-                scos.sort(key=lambda x: x[1])
-                betterHalf = scos[:max(3, int(len(scos)/3))]
-                myScores = [bh[0]**2 for bh in betterHalf]
-                strongs[p] = sqrt(myScores[0]*0.75 +
-                                  sum(myScores)/(len(myScores)*4))
-        update = False
-        for s in range(self.playersNum):
-            if strongs[s] != self._strongs[s]:
-                update = True
-                break
-        self._strongs = strongs
-        if update:
-            if self.parent != None:
-                cascade = self.parent._pullStrong()
-            else:
-                cascade = 2
-            self._cascadeMemory = self._cascadeMemory/2 + cascade
-            return cascade + 1
-        self._cascadeMemory /= 2
-        return 0
-
-    def forceStrong(self, depth=3):
-        if depth == 0:
-            self.strongDecay()
-        else:
-            if len(self.childs):
-                for c in self.childs:
-                    c.forceStrong(depth-1)
-            else:
-                self.strongDecay()
-
-    def decayEvent(self):
-        for c in self.childs:
-            c.strongDecay()
-
-    def strongDecay(self):
-        if self._strongs == [None]*self.playersNum:
-            if not self.scoresAvaible():
-                self._calcScores()
-            self._strongs = self._scores
-            if self.parent:
-                return self.parent._pullStrong()
-            return 1
-        return None
-
-    def getSelfScore(self):
-        return self.getScoreFor(self.curPlayer)
-
-    def getScoreFor(self, player):
-        if self._scores[player] == None:
-            self._calcScore(player)
-        return self._scores[player]
-
-    def scoreAvaible(self, player):
-        return self._scores[player] != None
-
-    def scoresAvaible(self):
-        for p in self._scores:
-            if p == None:
-                return False
-        return True
-
-    def strongScoresAvaible(self):
-        for p in self._strongs:
-            if p == None:
-                return False
-        return True
-
-    def askUserForAction(self):
-        return self.state.askUserForAction(self.avaibleActions)
-
-    def _calcScores(self):
-        for p in range(self.state.playersNum):
-            self._calcScore(p)
-
-    def _calcScore(self, player):
-        winner = self._getWinner()
-        if winner != None:
-            if winner == player:
-                self._scores[player] = 0.0
-            elif winner == -1:
-                self._scores[player] = 2/3
-            else:
-                self._scores[player] = 1.0
-            return
-        if self.universe.scoreProvider == 'naive':
-            self._scores[player] = self.state.getScoreFor(player)
-        elif self.universe.scoreProvider == 'neural':
-            self._scores[player] = self.state.getScoreNeural(
-                self.universe.model, player)
-        else:
-            raise Exception('Uknown Score-Provider')
-
-    def getPriority(self):
-        return self.state.getPriority(self.getSelfScore(), self._cascadeMemory)
-
-    @property
-    def playersNum(self):
-        return self.state.playersNum
-
-    @property
-    def avaibleActions(self):
-        r = []
-        for c in self.childs:
-            r.append(c.lastAction)
-        return r
-
-    @property
-    def curPlayer(self):
-        return self.state.curPlayer
-
-    def _getWinner(self):
-        return self.state.checkWin()
-
-    def getWinner(self):
-        if len(self.childs) == 0:
-            return -1
-        return self._getWinner()
-
-    def _activateEdge(self, dist=0):
-        if not self.strongScoresAvaible():
-            self.universe.newOpen(self)
-        else:
-            for c in self.childs:
-                if c._cascadeMemory > 0.001*(dist-2) or random.random() < 0.01:
-                    c._activateEdge(dist=dist+1)
-
-    def __str__(self):
-        s = []
-        if self.lastAction == None:
-            s.append("[ {ROOT} ]")
-        else:
-            s.append("[ -> "+str(self.lastAction)+" ]")
-        s.append("[ turn: "+str(self.state.curPlayer)+" ]")
-        s.append(str(self.state))
-        s.append("[ score: "+str(self.getScoreFor(0))+" ]")
-        return '\n'.join(s)
-
-
-def choose(txt, options):
-    while True:
-        print('[*] '+txt)
-        for num, opt in enumerate(options):
-            print('['+str(num+1)+'] ' + str(opt))
-        inp = input('[> ')
-        try:
-            n = int(inp)
-            if n in range(1, len(options)+1):
-                return options[n-1]
-        except:
-            pass
-        for opt in options:
-            if inp == str(opt):
-                return opt
-        if len(inp) == 1:
-            for opt in options:
-                if inp == str(opt)[0]:
-                    return opt
-        print('[!] Invalid Input.')
-
-
-class Worker():
-    def __init__(self, universe):
-        self.universe = universe
-        self._alive = True
-
-    def run(self):
-        import threading
-        self.thread = threading.Thread(target=self.runLocal)
-        self.thread.start()
-
-    def runLocal(self):
-        for i, node in enumerate(self.universe.iter()):
-            if node == None:
-                time.sleep(1)
-            if not self._alive:
-                return
-            node.decayEvent()
-
-    def kill(self):
-        self._alive = False
-        self.thread.join(15)
-
-    def revive(self):
-        self._alive = True
-
-
-class Runtime():
-    def __init__(self, initState):
-        universe = QueueingUniverse()
-        self.head = Node(initState, universe=universe)
-        _ = self.head.childs
-        universe.newOpen(self.head)
-
-    def spawnWorker(self):
-        self.worker = Worker(self.head.universe)
-        self.worker.run()
-
-    def killWorker(self):
-        self.worker.kill()
-
-    def performAction(self, action):
-        for c in self.head.childs:
-            if action == c.lastAction:
-                self.head.universe.clearPQ()
-                self.head.kill()
-                self.head = c
-                self.head.universe.activateEdge(self.head)
-                return
-        raise Exception('No such action avaible...')
-
-    def turn(self, bot=None, calcDepth=3, bg=True):
-        print(str(self.head))
-        if bot == None:
-            c = choose('Select action?', ['human', 'bot', 'undo', 'qlen'])
-            if c == 'undo':
-                self.head = self.head.parent
-                return
-            elif c == 'qlen':
-                print(self.head.universe.pq.qsize())
-                return
-            bot = c == 'bot'
-        if bot:
-            self.head.forceStrong(calcDepth)
-            opts = []
-            for c in self.head.childs:
-                opts.append((c, c.getStrongFor(self.head.curPlayer)))
-            opts.sort(key=lambda x: x[1])
-            print('[i] Evaluated Options:')
-            for o in opts:
-                #print('['+str(o[0])+']' + str(o[0].lastAction) + " (Score: "+str(o[1])+")")
-                print('[ ]' + str(o[0].lastAction) + " (Score: "+str(o[1])+")")
-            print('[#] I choose to play: ' + str(opts[0][0].lastAction))
-            self.performAction(opts[0][0].lastAction)
-        else:
-            action = self.head.askUserForAction()
-            self.performAction(action)
-
-    def game(self, bots=None, calcDepth=7, bg=True):
-        if bg:
-            self.spawnWorker()
-        if bots == None:
-            bots = [None]*self.head.playersNum
-        while self.head.getWinner() == None:
-            self.turn(bots[self.head.curPlayer], calcDepth, bg=True)
-        print(['O', 'X', 'No one'][self.head.getWinner()] + ' won!')
-        if bg:
-            self.killWorker()
-
-    def saveModel(self, model, gen):
-        dat = model.state_dict()
-        with open(self.getModelFileName(), 'wb') as f:
-            pickle.dump((gen, dat), f)
-
-    def loadModelState(self, model):
-        with open(self.getModelFileName(), 'rb') as f:
-            gen, dat = pickle.load(f)
-        model.load_state_dict(dat)
-        model.eval()
-        return gen
-
-    def loadModel(self):
-        model = self.head.state.getModel()
-        gen = self.loadModelState(model)
-        return model, gen
-
-    def getModelFileName(self):
-        return 'brains/utt.vac'
-
-    def saveToMemoryBank(self, term):
-        return
-        with open('memoryBank/uttt/'+datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')+'_'+str(int(random.random()*99999))+'.vdm', 'wb') as f:
-            pickle.dump(term, f)
-
-
-class NeuralRuntime(Runtime):
-    def __init__(self, initState):
-        super().__init__(initState)
-
-        model, gen = self.loadModel()
-
-        self.head.universe.model = model
-        self.head.universe.scoreProvider = 'neural'
-
-
-class Trainer(Runtime):
-    def __init__(self, initState):
-        super().__init__(initState)
-        #self.universe = Universe()
-        self.universe = self.head.universe
-        self.rootNode = self.head
-        self.terminal = None
-
-    def buildDatasetFromModel(self, model, depth=4, refining=True, fanOut=[5, 5, 5, 5, 4, 4, 4, 4], uncertainSec=15, exacity=5):
-        print('[*] Building Timeline')
-        term = self.linearPlay(model, calcDepth=depth, exacity=exacity)
-        if refining:
-            print('[*] Refining Timeline (exploring alternative endings)')
-            cur = term
-            for d in fanOut:
-                cur = cur.parent
-                cur.forceStrong(d)
-                print('.', end='', flush=True)
-            print('')
-            print('[*] Refining Timeline (exploring uncertain regions)')
-            self.timelineExpandUncertain(term, uncertainSec)
-        return term
-
-    def linearPlay(self, model, calcDepth=7, exacity=5, verbose=False, firstNRandom=2):
-        head = self.rootNode
-        self.universe.model = model
-        self.spawnWorker()
-        while head.getWinner() == None:
-            if verbose:
-                print(head)
-            else:
-                print('.', end='', flush=True)
-            head.forceStrong(calcDepth)
-            opts = []
-            if len(head.childs) == 0:
-                break
-            for c in head.childs:
-                opts.append((c, c.getStrongFor(head.curPlayer)))
-            if firstNRandom:
-                firstNRandom -= 1
-                ind = int(random.random()*len(opts))
-            else:
-                opts.sort(key=lambda x: x[1])
-                if exacity >= 10:
-                    ind = 0
-                else:
-                    ind = int(pow(random.random(), exacity)*(len(opts)-1))
-            head = opts[ind][0]
-        self.killWorker()
-        if verbose:
-            print(head)
-        print(' => '+['O', 'X', 'No one'][head.getWinner()] + ' won!')
-        return head
-
-    def timelineIterSingle(self, term):
-        for i in self.timelineIter(self, [term]):
-            yield i
-
-    def timelineIter(self, terms, altChildPerNode=-1):
-        batch = len(terms)
-        heads = terms
-        while True:
-            empty = True
-            for b in range(batch):
-                head = heads[b]
-                if head == None:
-                    continue
-                empty = False
-                yield head
-                if len(head.childs):
-                    if altChildPerNode == -1:  # all
-                        for child in head.childs:
-                            yield child
-                    else:
-                        for j in range(min(altChildPerNode, int(len(head.childs)/2))):
-                            yield random.choice(head.childs)
-                if head.parent == None:
-                    head = None
-                else:
-                    head = head.parent
-                heads[b] = head
-            if empty:
-                return
-
-    def timelineExpandUncertain(self, term, secs):
-        self.rootNode.universe.clearPQ()
-        self.rootNode.universe.activateEdge(self.rootNode)
-        self.spawnWorker()
-        for s in range(secs):
-            time.sleep(1)
-            print('.', end='', flush=True)
-        self.rootNode.universe.clearPQ()
-        self.killWorker()
-        print('')
-
-    def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, terms=None, batch=16):
-        loss_func = nn.MSELoss()
-        optimizer = optim.Adam(model.parameters(), lr)
-        if terms == None:
-            terms = []
-            for i in range(batch):
-                terms.append(self.buildDatasetFromModel(
-                    model, depth=calcDepth, exacity=exacity))
-        print('[*] Conditioning Brain')
-        for r in range(64):
-            loss_sum = 0
-            lLoss = 0
-            zeroLen = 0
-            for i, node in enumerate(self.timelineIter(terms)):
-                for p in range(self.rootNode.playersNum):
-                    inp = node.state.getTensor(player=p)
-                    gol = torch.tensor(
-                        [node.getStrongFor(p)], dtype=torch.float)
-                    out = model(inp)
-                    loss = loss_func(out, gol)
-                    optimizer.zero_grad()
-                    loss.backward()
-                    optimizer.step()
-                    loss_sum += loss.item()
-                    if loss.item() == 0.0:
-                        zeroLen += 1
-                    if zeroLen == 5:
-                        break
-            # print(loss_sum/i)
-            if r > 16 and (loss_sum/i < cut or lLoss == loss_sum):
-                return loss_sum
-            lLoss = loss_sum
-        return loss_sum
-
-    def main(self, model=None, gens=1024, startGen=0):
-        newModel = False
-        if model == None:
-            print('[!] No brain found. Creating new one...')
-            newModel = True
-            model = self.rootNode.state.getModel()
-        self.universe.scoreProvider = ['neural', 'naive'][newModel]
-        model.train()
-        for gen in range(startGen, startGen+gens):
-            print('[#####] Gen '+str(gen)+' training:')
-            loss = self.trainModel(model, calcDepth=min(
-                4, 3+int(gen/16)), exacity=int(gen/3+1), batch=4)
-            print('[L] '+str(loss))
-            self.universe.scoreProvider = 'neural'
-            self.saveModel(model, gen)
-
-    def trainFromTerm(self, term):
-        model, gen = self.loadModel()
-        self.universe.scoreProvider = 'neural'
-        self.trainModel(model, calcDepth=4, exacity=10, term=term)
-        self.saveModel(model)
-
-    def train(self):
-        if os.path.exists(self.getModelFileName()):
-            model, gen = self.loadModel()
-            self.main(model, startGen=gen+1)
-        else:
-            self.main()

vacuumDecay/__init__.py

@@ -0,0 +1,4 @@
+from vacuumDecay.runtime import Runtime, NeuralRuntime, Trainer
+from vacuumDecay.base import Node, State, Action, Universe, QueueingUniverse
+from vacuumDecay.utils import choose
+from vacuumDecay.run import main

vacuumDecay/base.py

@@ -0,0 +1,399 @@
+import torch
+import time
+import random
+from math import sqrt
+from abc import ABC, abstractmethod
+from queue import PriorityQueue, Empty
+from dataclasses import dataclass, field
+from typing import Any
+from torch import nn
+import torch.nn.functional as F
+
+from vacuumDecay.utils import choose
+
+class Action():
+    # Should hold the data representing an action
+    # Actions are applied to a State in State.mutate
+    def __init__(self, player, data):
+        self.player = player
+        self.data = data
+
+    # ImproveMe
+    def __eq__(self, other):
+        # This should be implemented differently
+        # Two actions of different generations will never be compared
+        if type(other) != type(self):
+            return False
+        return str(self.data) == str(other.data)
+
+    # ImproveMe
+    def __str__(self):
+        # should return visual representation of this action
+        # should start with < and end with >
+        return "<P"+str(self.player)+"-"+str(self.data)+">"
+
+    # ImproveMe
+    def getImage(self, state):
+        # Should return an image representation of this action given the current state
+        # Return None if not implemented
+        return None
+
+    # ImproveMe
+    def getTensor(self, state, player=None):
+        # Should return a complete description of the action (including previous state)
+        # This default will work, but may be suboptimal...
+        return (state.getTensor(), state.mutate(self).getTensor())
+
+class State(ABC):
+    # Hold a representation of the current game-state
+    # Allows retriving avaible actions (getAvaibleActions) and applying them (mutate)
+    # Mutations return a new State and should not have any effect on the current State
+    # Allows checking itself for a win (checkWin) or scoring itself based on a simple heuristic (getScore)
+    # The calculated score should be 0 when won; higher when in a worse state; highest for loosing
+    # getPriority is used for prioritising certain Nodes / States when expanding / walking the tree (TODO: Remove)
+
+    # Abstract Methodas need to be overrieden, improveMe methods can be overrieden
+
+    def __init__(self, curPlayer=0, generation=0, playersNum=2):
+        self.curPlayer = curPlayer
+        self.generation = generation
+        self.playersNum = playersNum
+
+    @abstractmethod
+    def mutate(self, action):
+        # Returns a new state with supplied action performed
+        # self should not be changed
+        return State(curPlayer=(self.curPlayer+1) % self.playersNum, generation=self.generation+1, playersNum=self.playersNum)
+
+    @abstractmethod
+    def getAvaibleActions(self):
+        # Should return an array of all possible actions
+        return []
+
+    def askUserForAction(self, actions):
+        return choose('What does player '+str(self.curPlayer)+' want to do?', actions)
+
+    # improveMe
+    def getPriority(self, score, cascadeMemory):
+        # Used for ordering the priority queue
+        # Priority should not change for the same root
+        # Lower prioritys get worked on first
+        # Higher generations should have higher priority
+        # Higher cascadeMemory (more influence on higher-order-scores) should have lower priority
+        return -cascadeMemory + 100
+
+    @abstractmethod
+    def checkWin(self):
+        # -1 -> Draw
+        # None -> Not ended
+        # n e N -> player n won
+        return None
+
+    # improveMe
+    def getScoreFor(self, player):
+        # 0 <= score <= 1; should return close to zero when we are winning
+        w = self.checkWin()
+        if w == None:
+            return 0.5
+        if w == player:
+            return 1
+        if w == -1:
+            return 0.1
+        return 0
+
+    @abstractmethod
+    def __str__(self):
+        # return visual rep of state
+        return "[#]"
+
+    @abstractmethod
+    def getTensor(self, player=None):
+        if player == None:
+            player = self.curPlayer
+        return torch.tensor([0])
+
+    @classmethod
+    def getVModel(cls):
+        # input will be output from state.getTensor
+        pass
+
+    #improveMe
+    def getQModel(cls):
+        # input will be output from action.getTensor
+        return DefaultQ(cls.getVModel())
+
+    def getScoreNeural(self, model, player=None):
+        return model(self.getTensor(player=player)).item()
+
+    # improveMe
+    def getImage(self):
+        # Should return an image representation of this state
+        # Return None if not implemented
+        return None
+
+class DefaultQ(nn.Module):
+    def __init__(self, vModel):
+        super().__init__()
+        self.V = vModel
+    
+    def forward(self, inp):
+        s, s_prime = inp
+        v, v_prime = self.V(s), self.V(s_prime)        
+        return F.sigmoid(v_prime - v)
+
+class Universe():
+    def __init__(self):
+        self.scoreProvider = 'naive'
+
+    def newOpen(self, node):
+        pass
+
+    def merge(self, node):
+        return node
+
+    def clearPQ(self):
+        pass
+
+    def iter(self):
+        return []
+
+    def activateEdge(self, head):
+        pass
+
+
+@dataclass(order=True)
+class PQItem:
+    priority: int
+    data: Any = field(compare=False)
+
+
+class QueueingUniverse(Universe):
+    def __init__(self):
+        super().__init__()
+        self.pq = PriorityQueue()
+
+    def newOpen(self, node):
+        item = PQItem(node.getPriority(), node)
+        self.pq.put(item)
+
+    def merge(self, node):
+        self.newOpen(node)
+        return node
+
+    def clearPQ(self):
+        self.pq = PriorityQueue()
+
+    def iter(self):
+        while True:
+            try:
+                yield self.pq.get(False).data
+            except Empty:
+                return None
+
+    def activateEdge(self, head):
+        head._activateEdge()
+
+class Node:
+    def __init__(self, state, universe=None, parent=None, lastAction=None):
+        self.state = state
+        if universe == None:
+            print('[!] No Universe defined. Spawning one...')
+            universe = Universe()
+        self.universe = universe
+        self.parent = parent
+        self.lastAction = lastAction
+
+        self._childs = None
+        self._scores = [None]*self.state.playersNum
+        self._strongs = [None]*self.state.playersNum
+        self._alive = True
+        self._cascadeMemory = 0  # Used for our alternative to alpha-beta pruning
+        self._winner = -2
+
+        self.leaf = True
+        self.last_updated = time.time()  # New attribute
+
+    def mark_update(self):
+        self.last_updated = time.time()
+
+    def kill(self):
+        self._alive = False
+
+    def revive(self):
+        self._alive = True
+
+    @property
+    def childs(self):
+        if self._childs == None:
+            self._expand()
+        return self._childs
+
+    def _expand(self):
+        self.leaf = False
+        self._childs = []
+        actions = self.state.getAvaibleActions()
+        for action in actions:
+            newNode = Node(self.state.mutate(action),
+                           self.universe, self, action)
+            self._childs.append(self.universe.merge(newNode))
+        self.mark_update()
+
+    def getStrongFor(self, player):
+        if self._strongs[player] != None:
+            return self._strongs[player]
+        else:
+            return self.getScoreFor(player)
+
+    def _pullStrong(self):
+        strongs = [None]*self.playersNum
+        has_winner = self.getWinner() != None
+        for p in range(self.playersNum):
+            cp = self.state.curPlayer
+            if has_winner:
+                strongs[p] = self.getScoreFor(p)
+            elif cp == p:
+                best = float('-inf')
+                for c in self.childs:
+                    if c.getStrongFor(p) > best:
+                        best = c.getStrongFor(p)
+                strongs[p] = best
+            else:
+                scos = [(c.getStrongFor(p), c.getStrongFor(cp)) for c in self.childs]
+                scos.sort(key=lambda x: x[1], reverse=True)
+                betterHalf = [sco for sco, osc in scos[:max(3, int(len(scos)/2))]]
+                strongs[p] = betterHalf[0]*0.9 + sum(betterHalf)/(len(betterHalf))*0.1
+        update = False
+        for s in range(self.playersNum):
+            if strongs[s] != self._strongs[s]:
+                update = True
+                break
+        self._strongs = strongs
+        if update:
+            if self.parent != None:
+                cascade = self.parent._pullStrong()
+            else:
+                cascade = 2
+            self._cascadeMemory = self._cascadeMemory/2 + cascade
+            self.mark_update()
+            return cascade + 1
+        self._cascadeMemory /= 2
+        return 0
+
+    def forceStrong(self, depth=3):
+        if depth == 0:
+            self.strongDecay()
+        else:
+            if len(self.childs):
+                for c in self.childs:
+                    c.forceStrong(depth-1)
+            else:
+                self.strongDecay()
+
+    def decayEvent(self):
+        for c in self.childs:
+            c.strongDecay()
+
+    def strongDecay(self):
+        if self._strongs == [None]*self.playersNum:
+            if not self.scoresAvaible():
+                self._calcScores()
+            self._strongs = self._scores
+            if self.parent:
+                return self.parent._pullStrong()
+            return 1
+        return None
+
+    def getSelfScore(self):
+        return self.getScoreFor(self.curPlayer)
+
+    def getScoreFor(self, player):
+        if self._scores[player] == None:
+            self._calcScore(player)
+        return self._scores[player]
+
+    def scoreAvaible(self, player):
+        return self._scores[player] != None
+
+    def scoresAvaible(self):
+        for p in self._scores:
+            if p == None:
+                return False
+        return True
+
+    def strongScoresAvaible(self):
+        for p in self._strongs:
+            if p == None:
+                return False
+        return True
+
+    def askUserForAction(self):
+        return self.state.askUserForAction(self.avaibleActions)
+
+    def _calcScores(self):
+        for p in range(self.state.playersNum):
+            self._calcScore(p)
+
+    def _calcScore(self, player):
+        winner = self._getWinner()
+        if winner != None:
+            if winner == player:
+                self._scores[player] = 1.0
+            elif winner == -1:
+                self._scores[player] = 0.1
+            else:
+                self._scores[player] = 0.0
+            return
+        if self.universe.scoreProvider == 'naive':
+            self._scores[player] = self.state.getScoreFor(player)
+        elif self.universe.scoreProvider == 'neural':
+            self._scores[player] = self.state.getScoreNeural(self.universe.v_model, player)
+        else:
+            raise Exception('Unknown Score-Provider')
+
+    def getPriority(self):
+        return self.state.getPriority(self.getSelfScore(), self._cascadeMemory)
+
+    @property
+    def playersNum(self):
+        return self.state.playersNum
+
+    @property
+    def avaibleActions(self):
+        r = []
+        for c in self.childs:
+            r.append(c.lastAction)
+        return r
+
+    @property
+    def curPlayer(self):
+        return self.state.curPlayer
+
+    def _getWinner(self):
+        return self.state.checkWin()
+
+    def getWinner(self):
+        if len(self.childs) == 0:
+            return -1
+        if self._winner==-2:
+            self._winner = self._getWinner()
+        return self._winner
+
+    def _activateEdge(self, dist=0):
+        if not self.strongScoresAvaible():
+            self.universe.newOpen(self)
+        else:
+            for c in self.childs:
+                if c._cascadeMemory > 0.001*(dist-2) or random.random() < 0.01:
+                    c._activateEdge(dist=dist+1)
+        self.mark_update()
+
+    def __str__(self):
+        s = []
+        if self.lastAction == None:
+            s.append("[ {ROOT} ]")
+        else:
+            s.append("[ -> "+str(self.lastAction)+" ]")
+        s.append("[ turn: "+str(self.state.curPlayer)+" ]")
+        s.append(str(self.state))
+        s.append("[ score: "+str(self.getScoreFor(0))+" ]")
+        return '\n'.join(s)

vacuumDecay/games/chess_game.py

@@ -0,0 +1,248 @@
+import numpy as np
+import torch as th
+from torch import nn
+import torch.nn.functional as F
+from PIL import Image
+import chess
+import chess.svg
+import io
+
+from vacuumDecay import State, Action, Runtime, NeuralRuntime, Trainer, choose, main
+
+class ChessAction(Action):
+    def __init__(self, player, data):
+        super().__init__(player, data)
+
+    def __str__(self):
+        return "<P"+str(self.player)+"-"+self.data.uci()+">"
+
+    def getImage(self, state=None):
+        return Image.open(io.BytesIO(chess.svg.board(board=state.board, format='png', squares=[self.data.from_square, self.data.to_square], arrows=[self.move])))
+
+    def getTensor(self, state):
+        board, additionals = state.getTensor()
+
+        tensor = np.zeros((8, 8), dtype=int)  # 13 channels for piece types and move squares
+        
+        # Mark the from_square and to_square
+        from_row, from_col = divmod(self.data.from_square, 8)
+        to_row, to_col = divmod(self.data.to_square, 8)
+        
+        tensor[from_row, from_col] = 1  # Mark the "from" square
+        tensor[to_row, to_col] = 1  # Mark the "to" square
+        
+        # Get the piece that was moved
+        pieceT = np.zeros((12), dtype=int)  # 13 channels for piece types and move squares
+        piece = state.board.piece_at(self.data.from_square)
+        if piece:
+            piece_type = {
+                'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5,
+                'P': 6, 'N': 7, 'B': 8, 'R': 9, 'Q': 10, 'K': 11
+            }
+            pieceT[piece_type[piece.symbol()]] = 1
+        
+        # Flatten the tensor and return as a PyTorch tensor
+        return (board, additionals, th.concat(tensor.flatten(), pieceT.flatten()))
+
+piece_values = {
+    chess.PAWN: 1,
+    chess.KNIGHT: 3,
+    chess.BISHOP: 3,
+    chess.ROOK: 5,
+    chess.QUEEN: 9
+}
+
+class ChessState(State):
+    def __init__(self, curPlayer=0, generation=0, board=None):
+        if type(board) == type(None):
+            board = chess.Board()
+        self.curPlayer = curPlayer
+        self.generation = generation
+        self.playersNum = 2
+        self.board = board
+
+    def mutate(self, action):
+        newBoard = self.board.copy()
+        newBoard.push(action.data)
+        return ChessState(curPlayer=(self.curPlayer+1)%2, board=newBoard)
+
+    # Function to calculate total value of pieces for a player
+    def calculate_piece_value(self, board, color):
+        value = 0
+        for square in chess.scan_reversed(board.occupied_co[color]):
+            piece = board.piece_at(square)
+            if piece is not None:
+                value += piece_values.get(piece.piece_type, 0)
+        return value
+
+    # Function to calculate winning probability for each player
+    def calculate_winning_probability(self):
+        white_piece_value = self.calculate_piece_value(self.board, chess.WHITE)
+        black_piece_value = self.calculate_piece_value(self.board, chess.BLACK)
+        total_piece_value = white_piece_value + black_piece_value
+        
+        # Calculate winning probabilities
+        white_probability = white_piece_value / total_piece_value
+        black_probability = black_piece_value / total_piece_value
+        
+        return white_probability, black_probability
+
+    def getScoreFor(self, player):
+        w = self.checkWin()
+        if w == None:
+            return self.calculate_winning_probability()[player]
+        if w == player:
+            return 1
+        if w == -1:
+            return 0.1
+        return 0
+
+    def getAvaibleActions(self):
+        for move in self.board.legal_moves:
+            yield ChessAction(self.curPlayer, move)
+
+    def checkWin(self):
+        if self.board.is_checkmate():
+            return (self.curPlayer+1)%2
+        elif self.board.is_stalemate():
+            return -1
+        return None
+
+    def __str__(self):
+        return str(self.board)
+
+    def getTensor(self):
+        board = self.board
+        piece_to_plane = {
+            'P': 0, 'N': 1, 'B': 2, 'R': 3, 'Q': 4, 'K': 5,
+            'p': 6, 'n': 7, 'b': 8, 'r': 9, 'q': 10, 'k': 11
+        }
+        
+        tensor = np.zeros((12, 8, 8), dtype=int)
+        for square in chess.SQUARES:
+            piece = board.piece_at(square)
+            if piece:
+                plane = piece_to_plane[piece.symbol()]
+                row, col = divmod(square, 8)
+                tensor[plane, row, col] = 1
+
+        # Side to move
+        side_to_move = np.array([1 if board.turn == chess.WHITE else 0])
+        
+        # Castling rights
+        castling_rights = np.array([
+            1 if board.has_kingside_castling_rights(chess.WHITE) else 0,
+            1 if board.has_queenside_castling_rights(chess.WHITE) else 0,
+            1 if board.has_kingside_castling_rights(chess.BLACK) else 0,
+            1 if board.has_queenside_castling_rights(chess.BLACK) else 0
+        ])
+        
+        # En passant target square
+        en_passant = np.zeros((8, 8), dtype=int)
+        if board.ep_square:
+            row, col = divmod(board.ep_square, 8)
+            en_passant[row, col] = 1
+        
+        # Half-move clock and full-move number
+        half_move_clock = np.array([board.halfmove_clock])
+        full_move_number = np.array([board.fullmove_number])
+        
+        additionals = np.concatenate([
+            side_to_move,
+            castling_rights,
+            en_passant.flatten(),
+            half_move_clock,
+            full_move_number
+        ])
+
+        return (th.tensor(tensor), th.tensor(additionals))
+
+    @classmethod
+    def getVModel():
+        return ChessV()
+
+    @classmethod
+    def getQModel():
+        return ChessQ()
+
+    def getImage(self):
+        return Image.open(io.BytesIO(chess.svg.board(board=self.board, format='png')))
+
+class ChessV(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # CNN for the board tensor
+        self.conv1 = nn.Conv2d(12, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(32 * 8 * 8, 256)
+
+        # FCNN for the board tensor
+        self.fc2 = nn.Linear(8 * 8, 64)
+
+        # FCNN for additional info
+        self.fc_additional1 = nn.Linear(71, 64)
+        
+        # Combine all outputs
+        self.fc_combined1 = nn.Linear(256 + 64 + 64, 128)
+        self.fc_combined2 = nn.Linear(128, 1)
+    
+    def forward(self, inp):
+        board_tensor, additional_info = inp
+        # Process the board tensor through the CNN
+        x = F.relu(self.conv1(board_tensor))
+        x = F.relu(self.conv2(x))
+        x = x.view(x.size(0), -1)  # Flatten the tensor
+        x = F.relu(self.fc1(x))
+        
+        y = F.relu(self.fc2(board_tensor.view(board_tensor.size(0), -1)))
+
+        # Process the additional info through the FCNN
+        z = F.relu(self.fc_additional1(additional_info))
+        
+        # Combine the outputs
+        combined = th.cat((x, y, z), dim=1)
+        combined = F.relu(self.fc_combined1(combined))
+        logit = self.fc_combined2(combined)
+        
+        return logit
+
+class ChessQ(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # CNN for the board tensor
+        self.conv1 = nn.Conv2d(12, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(32 * 8 * 8, 256)
+
+        # FCNN for the board tensor
+        self.fc2 = nn.Linear(8 * 8, 64)
+
+        # FCNN for additional info
+        self.fc_additional1 = nn.Linear(71, 64)
+        
+        # Combine all outputs
+        self.fc_combined1 = nn.Linear(256 + 64 + 64, 128)
+        self.fc_combined2 = nn.Linear(128, 1)
+    
+    def forward(self, inp):
+        board_tensor, additional_info, action = inp
+        # Process the board tensor through the CNN
+        x = F.relu(self.conv1(board_tensor))
+        x = F.relu(self.conv2(x))
+        x = x.view(x.size(0), -1)  # Flatten the tensor
+        x = F.relu(self.fc1(x))
+        
+        y = F.relu(self.fc2(board_tensor.view(board_tensor.size(0), -1)))
+
+        # Process the additional info through the FCNN
+        z = F.relu(self.fc_additional1(additional_info))
+        
+        # Combine the outputs
+        combined = th.cat((x, y, z), dim=1)
+        combined = F.relu(self.fc_combined1(combined))
+        logit = self.fc_combined2(combined)
+        
+        return logit
+
+if __name__=="__main__":
+    main(ChessState, start_visualizer=False)

vacuumDecay/games/mars_cubes.py

@@ -1,23 +1,46 @@
 from vacuumDecay import *
 import numpy as np
+from enum import Enum
 
-class TTTState(State):
-    def __init__(self, curPlayer=0, generation=0, playersNum=2, board=None):
-        if type(board) == type(None):
-            board = np.array([None]*9)
-        self.curPlayer = curPlayer
+
+class Face(Enum):
+    TANK = 1
+    LASER = 2
+    HUMAN = 3
+    COW = 4
+    CHICKEN = 5
+
+    @property
+    def num_faces(self):
+        return 2 if self == Face.LASER else 1
+
+    @property
+    def prob(self):
+        return self.num_faces/6
+
+    @property
+    def is_collectable(self):
+        return not self in [Face.TANK, Face.LASER]
+
+    @property
+    def force_pickup(self):
+        return self in [Face.TANK]
+
+
+class MCState(State):
+    def __init__(self, generation=0, hand_dices_num=12, table_dices=[0]*5):
         self.generation = generation
-        self.playersNum = playersNum
-        self.board = board
+        self.hand_dices_num = hand_dices_num
+        self.table_dices = table_dices
 
     def mutate(self, action):
         newBoard = np.copy(self.board)
         newBoard[action.data] = self.curPlayer
-        return TTTState(curPlayer=(self.curPlayer+1)%self.playersNum, playersNum=self.playersNum, board=newBoard)
+        return MCState(curPlayer=(self.curPlayer+1) % self.playersNum, playersNum=self.playersNum, board=newBoard)
 
     def getAvaibleActions(self):
         for i in range(9):
-            if self.board[i]==None:
+            if self.board[i] == None:
                 yield Action(self.curPlayer, i)
 
     def checkWin(self):
@@ -39,7 +62,8 @@ class TTTState(State):
     def __str__(self):
         s = []
         for l in range(3):
-            s.append(" ".join([str(p) if p!=None else '.' for p in self.board[l*3:][:3]]))
+            s.append(
+                " ".join([str(p) if p != None else '.' for p in self.board[l*3:][:3]]))
         return "\n".join(s)
 
     def getTensor(self):
@@ -52,9 +76,10 @@ class TTTState(State):
             torch.nn.ReLu(),
             torch.nn.Linear(10, 3),
             torch.nn.Sigmoid(),
-            torch.nn.Linear(3,1)
+            torch.nn.Linear(3, 1)
         )
 
-if __name__=="__main__":
-    run = Runtime(TTTState())
+
+if __name__ == "__main__":
+    run = Runtime(MCState())
     run.game()

vacuumDecay/games/tictactoe.py

@@ -0,0 +1,116 @@
+import numpy as np
+import torch
+from PIL import Image, ImageDraw
+
+from vacuumDecay import State, Action, Runtime, NeuralRuntime, Trainer, choose, main
+
+class TTTAction(Action):
+    def __init__(self, player, data):
+        super().__init__(player, data)
+
+    def getImage(self, state=None):
+        # Should return an image representation of this action given the current state
+        if state is None or not isinstance(state, TTTState):
+            return None
+
+        img = state.getImage()
+        if img is not None:
+            draw = ImageDraw.Draw(img)
+            x = (self.data % 3) * 100 + 50
+            y = (self.data // 3) * 100 + 50
+            if self.player == 0:
+                draw.ellipse((x-40, y-40, x+40, y+40), outline='blue', width=2)
+            else:
+                draw.line((x-40, y-40, x+40, y+40), fill='red', width=2)
+                draw.line((x+40, y-40, x-40, y+40), fill='red', width=2)
+        return img
+
+    def getTensor(self, state, player=None):
+        return torch.concat(torch.tensor([self.turn]), torch.tensor(state.board), torch.tensor(state.mutate(self).board))
+
+class TTTState(State):
+    def __init__(self, curPlayer=0, generation=0, playersNum=2, board=None):
+        if type(board) == type(None):
+            board = np.array([None]*9)
+        self.curPlayer = curPlayer
+        self.generation = generation
+        self.playersNum = playersNum
+        self.board = board
+
+    def mutate(self, action):
+        newBoard = np.copy(self.board)
+        newBoard[action.data] = self.curPlayer
+        return TTTState(curPlayer=(self.curPlayer+1)%self.playersNum, playersNum=self.playersNum, board=newBoard)
+
+    def getAvaibleActions(self):
+        for i in range(9):
+            if self.board[i]==None:
+                yield TTTAction(self.curPlayer, i)
+
+    def checkWin(self):
+        s = self.board
+        for i in range(3):
+            if (s[i] == s[i+3] == s[i+6] != None):
+                return s[i]
+            if (s[i*3] == s[i*3+1] == s[i*3+2] != None):
+                return s[i*3]
+        if (s[0] == s[4] == s[8] != None):
+            return s[0]
+        if (s[2] == s[4] == s[6] != None):
+            return s[2]
+        for i in range(9):
+            if s[i] == None:
+                return None
+        return -1
+
+    def __str__(self):
+        s = []
+        for l in range(3):
+            s.append(" ".join([str(p) if p!=None else '.' for p in self.board[l*3:][:3]]))
+        return "\n".join(s)
+
+    def getTensor(self, player=None):
+        return torch.concat(torch.tensor([self.curPlayer]), torch.tensor(self.board))
+
+    @classmethod
+    def getVModel(cls):
+        return torch.nn.Sequential(
+            torch.nn.Linear(10, 10),
+            torch.nn.ReLU(),
+            torch.nn.Linear(10, 3),
+            torch.nn.ReLU(),
+            torch.nn.Linear(3,1),
+            torch.nn.Sigmoid(),
+        )
+
+    @classmethod
+    def getQModel(cls):
+        return torch.nn.Sequential(
+            torch.nn.Linear(20, 12),
+            torch.nn.ReLU(),
+            torch.nn.Linear(12, 3),
+            torch.nn.ReLU(),
+            torch.nn.Linear(3,1),
+            torch.nn.Sigmoid(),
+        )
+
+    def getImage(self):
+        img = Image.new('RGB', (300, 300), color='white')
+        draw = ImageDraw.Draw(img)
+        for i in range(1, 3):
+            draw.line((0, 100*i, 300, 100*i), fill='black', width=2)
+            draw.line((100*i, 0, 100*i, 300), fill='black', width=2)
+
+        for i, mark in enumerate(self.board):
+            if mark is not None:
+                x = (i % 3) * 100 + 50
+                y = (i // 3) * 100 + 50
+                if mark == 0:
+                    draw.ellipse((x-40, y-40, x+40, y+40), outline='blue', width=2)
+                else:
+                    draw.line((x-40, y-40, x+40, y+40), fill='red', width=2)
+                    draw.line((x+40, y-40, x-40, y+40), fill='red', width=2)
+        return img
+
+if __name__=="__main__":
+    main(TTTState, start_visualizer=False)

vacuumDecay/games/ultimatetictactoe.py

@@ -1,12 +1,21 @@
 """
 A lot of this code was stolen from Pulkit Maloo (https://github.com/pulkitmaloo/Ultimate-Tic-Tac-Toe)
 """
+import numpy as np
+import torch
+from torch import nn
+from PIL import Image, ImageDraw
 
-from vacuumDecay import *
 from collections import Counter
 import itertools
 
-class TTTState(State):
+from vacuumDecay import State, Action, Runtime, NeuralRuntime, Trainer, choose, main
+
+class UTTTAction(Action):
+    def __init__(self, player, data):
+        super().__init__(player, data)
+
+class UTTTState(State):
     def __init__(self, curPlayer=0, generation=0, playersNum=2, board=None, lastMove=-1):
         if type(board) == type(None):
             board = "." * 81
@@ -40,12 +49,12 @@ class TTTState(State):
         return "."
 
     def mutate(self, action):
-        newBoard = self.board[:action.data] + ['O','X'][self.curPlayer] + self.board[action.data+1:]
-        return TTTState(curPlayer=(self.curPlayer+1)%self.playersNum, playersNum=self.playersNum, board=newBoard, lastMove=action.data)
+        newBoard = self.board[:action.data] + ['O',
+                                               'X'][self.curPlayer] + self.board[action.data+1:]
+        return UTTTState(curPlayer=(self.curPlayer+1) % self.playersNum, playersNum=self.playersNum, board=newBoard, lastMove=action.data)
 
     def box(self, x, y):
-        return index(x, y) // 9
-
+        return self.index(x, y) // 9
 
     def next_box(self, i):
         return i % 9
@@ -61,34 +70,22 @@ class TTTState(State):
     def getAvaibleActions(self):
         if self.last_move == -1:
             for i in range(9*9):
-                yield Action(self.curPlayer, i)
+                yield UTTTAction(self.curPlayer, i)
             return
 
         box_to_play = self.next_box(self.last_move)
         idxs = self.indices_of_box(box_to_play)
         if self.box_won[box_to_play] != ".":
-            pi_2d = [self.indices_of_box(b) for b in range(9) if self.box_won[b] == "."]
+            pi_2d = [self.indices_of_box(b) for b in range(
+                9) if self.box_won[b] == "."]
             possible_indices = list(itertools.chain.from_iterable(pi_2d))
         else:
             possible_indices = idxs
 
         for ind in possible_indices:
-            if self.board[ind]=='.':
+            if self.board[ind] == '.':
                 yield Action(self.curPlayer, ind)
 
-    #def getScoreFor(self, player):
-    #    p = ['O','X'][player]
-    #    sco = 5
-    #    for w in self.box_won:
-    #        if w==p:
-    #            sco += 1
-    #        elif w!='.':
-    #            sco -= 0.5
-    #    return 1/sco
-
-    #def getPriority(self, score, cascadeMem):
-    #    return -cascadeMem*1 + 100
-
     def checkWin(self):
         self.update_box_won()
         game_won = self.check_small_box(self.box_won)
@@ -106,7 +103,7 @@ class TTTState(State):
     def __str__(self):
         state = self.board
         acts = list(self.getAvaibleActions())
-        if len(acts)<=9:
+        if len(acts) <= 9:
             for i, act in enumerate(acts):
                 state = state[:act.data] + str(i+1) + state[act.data+1:]
         s = []
@@ -130,9 +127,8 @@ class TTTState(State):
         else:
             return 1.0 - 2.0 * self.curPlayer
 
-
     def getTensor(self, player=None, phase='default'):
-        if player==None:
+        if player == None:
             player = self.curPlayer
         s = ''
         for row in range(1, 10):
@@ -141,85 +137,106 @@ class TTTState(State):
         return torch.tensor([self.symbToNum(b) for b in s])
 
     @classmethod
-    def getModel(cls, phase='default'):
-        return Model()
+    def getVModel(cls, phase='default'):
+        return TTTV()
 
-class Model(nn.Module):
+    @classmethod
+    def getQModel(cls, phase='default'):
+        return TTTQ()
+
+
+class TTTV(nn.Module):
     def __init__(self):
         super().__init__()
 
+        self.chansPerSmol = 24
+        self.chansPerSlot = 8
+        self.chansComp = 8
+
         self.smol = nn.Sequential(
             nn.Conv2d(
                 in_channels=1,
-                out_channels=16,
-                kernel_size=(3,3),
+                out_channels=self.chansPerSmol,
+                kernel_size=(3, 3),
                 stride=3,
                 padding=0,
             ),
             nn.ReLU()
         )
-        #self.comb = nn.Sequential(
-        #    nn.Conv1d(
-        #        in_channels=24,
-        #        out_channels=8,
-        #        kernel_size=1,
-        #        stride=1,
-        #        padding=0,
-        #    ),
-        #    nn.ReLU()
-        #)
+        self.comb = nn.Sequential(
+            nn.Conv1d(
+                in_channels=self.chansPerSmol,
+                out_channels=self.chansPerSlot,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ReLU()
+        )
         self.out = nn.Sequential(
-            #nn.Linear(9*8, 32),
-            #nn.ReLU(),
-            #nn.Linear(32, 8),
-            #nn.ReLU(),
-            nn.Linear(16*9, 12),
+            nn.Linear(self.chansPerSlot*9, self.chansComp),
             nn.ReLU(),
-            nn.Linear(12, 1),
+            nn.Linear(self.chansComp, 1),
             nn.Sigmoid()
         )
 
     def forward(self, x):
-        x = torch.reshape(x, (1,9,9))
+        x = torch.reshape(x, (1, 9, 9))
         x = self.smol(x)
-        #x = torch.reshape(x, (24,9))
-        #x = self.comb(x)
+        x = torch.reshape(x, (self.chansPerSmol, 9))
+        x = self.comb(x)
         x = torch.reshape(x, (-1,))
         y = self.out(x)
         return y
 
-def humanVsAi(train=True, remember=False, depth=3, bots=[0,1], noBg=False):
-    init = TTTState()
-    run = NeuralRuntime(init)
-    run.game(bots, depth, bg=not noBg)
+class TTTQ(nn.Module):
+    def __init__(self):
+        super().__init__()
 
-    if remember or train:
-        trainer = Trainer(init)
-    if remember:
-        trainer.saveToMemoryBank(run.head)
-        print('[!] Your cognitive and strategic destinctiveness was added to my own! (Game inserted into memoryBank)')
-    if train:
-        print("[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
-        trainer.trainFromTerm(run.head)
-    print('[!] I have become smart! Destroyer of human Ultimate-TicTacToe players! (Neuristic update completed)')
-    print('[!] This marks the beginning of the end of humankind!')
-    print('[i] Thanks for playing! Goodbye...')
+        self.chansPerSmol = 24
+        self.chansPerSlot = 8
+        self.chansComp = 8
 
-def aiVsAiLoop():
-    init = TTTState()
-    trainer = Trainer(init)
-    trainer.train()
+        self.smol = nn.Sequential(
+            nn.Conv2d(
+                in_channels=2,
+                out_channels=self.chansPerSmol,
+                kernel_size=(3, 3),
+                stride=3,
+                padding=0,
+            ),
+            nn.ReLU()
+        )
+        self.comb = nn.Sequential(
+            nn.Conv1d(
+                in_channels=self.chansPerSmol,
+                out_channels=self.chansPerSlot,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ReLU()
+        )
+        self.out = nn.Sequential(
+            nn.Linear(self.chansPerSlot*9*2, self.chansComp),
+            nn.ReLU(),
+            nn.Linear(self.chansComp, 4),
+            nn.ReLU(),
+            nn.Linear(4, 1),
+            nn.Sigmoid()
+        )
 
-if __name__=='__main__':
-    options = ['Play Against AI','Play Against AI (AI begins)','Play Against AI (Fast Play)','Playground','Let AI train']
-    opt = choose('?', options)
-    if opt == options[0]:
-        humanVsAi()
-    elif opt == options[1]:
-        humanVsAi(bots[1,0])
-    elif opt == options[2]:
-        humanVsAi(depth=2,noBg=True)
-    elif opt == options[3]:
-        humanVsAi(bots=[None,None])
-    else:
-        aiVsAiLoop()
+    def forward(self, x):
+        a, b = x
+        a = torch.reshape(a, (1, 9, 9))
+        b = torch.reshape(b, (1, 9, 9))
+        x = torch.stack((a,b))
+        x = self.smol(x)
+        x = torch.reshape(x, (self.chansPerSmol, 9))
+        x = self.comb(x)
+        x = torch.reshape(x, (-1,))
+        y = self.out(x)
+        return y
+
+if __name__=="__main__":
+    main(UTTTState)

vacuumDecay/run.py

@@ -0,0 +1,47 @@
+from vacuumDecay.runtime import NeuralRuntime, Runtime, Trainer
+from vacuumDecay.utils import choose
+
+def humanVsAi(StateClass, train=True, remember=False, depth=3, bots=[0, 1], noBg=False, start_visualizer=False):
+    init = StateClass()
+    run = NeuralRuntime(init, start_visualizer=start_visualizer)
+    run.game(bots, depth, bg=not noBg)
+
+    if remember or train:
+        trainer = Trainer(init)
+    if remember:
+        trainer.saveToMemoryBank(run.head)
+        print('[!] Your cognitive and strategic distinctiveness was added to my own! (Game inserted into memoryBank)')
+    if train:
+        print("[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
+        trainer.trainFromTerm(run.head)
+    print('[!] I have become smart! Destroyer of human Ultimate-TicTacToe players! (Neuristic update completed)')
+    print('[!] This marks the beginning of the end of humankind!')
+    print('[i] Thanks for playing! Goodbye...')
+
+def aiVsAiLoop(StateClass, start_visualizer=False):
+    init = StateClass()
+    trainer = Trainer(init, start_visualizer=start_visualizer)
+    trainer.train()
+
+def humanVsNaive(StateClass, start_visualizer=False, calcDepth=7):
+    run = Runtime(StateClass(), start_visualizer=start_visualizer)
+    run.game(calcDepth=calcDepth)
+
+def main(StateClass, **kwargs):
+    options = ['Play Against AI',
+               'Play Against AI (AI begins)', 'Play Against AI (Fast Play)', 'Playground', 'Let AI train', 'Play against Naive']
+    opt = choose('?', options)
+    if opt == options[0]:
+        humanVsAi(StateClass,**kwargs)
+    elif opt == options[1]:
+        humanVsAi(StateClass, bots=[1, 0], **kwargs)
+    elif opt == options[2]:
+        humanVsAi(StateClass, depth=2, noBg=True, **kwargs)
+    elif opt == options[3]:
+        humanVsAi(StateClass, bots=[None, None], **kwargs)
+    elif opt == options[4]:
+        aiVsAiLoop(StateClass, **kwargs)
+    elif opt == options[5]:
+        humanVsNaive(StateClass, **kwargs)
+    else:
+        aiVsAiLoop(StateClass, **kwargs)

vacuumDecay/runtime.py

@@ -0,0 +1,325 @@
+import os
+import time
+import datetime
+import pickle
+import torch
+import torch.nn as nn
+from torch import optim
+from math import pow
+import random
+import datetime
+import pickle
+
+from vacuumDecay.base import QueueingUniverse, Node
+from vacuumDecay.utils import choose
+from vacuumDecay.visualizer import Visualizer
+
+class Worker():
+    def __init__(self, universe):
+        self.universe = universe
+        self._alive = True
+
+    def run(self):
+        import threading
+        self.thread = threading.Thread(target=self.runLocal)
+        self.thread.start()
+
+    def runLocal(self):
+        for i, node in enumerate(self.universe.iter()):
+            if node == None:
+                time.sleep(1)
+            if not self._alive:
+                return
+            node.decayEvent()
+
+    def kill(self):
+        self._alive = False
+        self.thread.join(15)
+
+    def revive(self):
+        self._alive = True
+
+class Runtime():
+    def __init__(self, initState, start_visualizer=False):
+        universe = QueueingUniverse()
+        self.head = Node(initState, universe=universe)
+        self.root = self.head
+        _ = self.head.childs
+        universe.newOpen(self.head)
+        if start_visualizer:
+            self.startVisualizer()
+
+    def startVisualizer(self):
+        self.visualizer = Visualizer(self)
+        self.visualizer.start()
+
+    def spawnWorker(self):
+        self.worker = Worker(self.head.universe)
+        self.worker.run()
+
+    def killWorker(self):
+        self.worker.kill()
+
+    def performAction(self, action):
+        for c in self.head.childs:
+            if action == c.lastAction:
+                self.head.universe.clearPQ()
+                self.head.kill()
+                self.head = c
+                self.head.universe.activateEdge(self.head)
+                return
+        raise Exception('No such action avaible...')
+
+    def turn(self, bot=None, calcDepth=3, bg=True):
+        print(str(self.head))
+        if bot == None:
+            c = choose('Select action?', ['human', 'bot', 'undo', 'qlen'])
+            if c == 'undo':
+                self.head = self.head.parent
+                return
+            elif c == 'qlen':
+                print(self.head.universe.pq.qsize())
+                return
+            bot = c == 'bot'
+        if bot:
+            self.head.forceStrong(calcDepth)
+            opts = []
+            for c in self.head.childs:
+                opts.append((c, c.getStrongFor(self.head.curPlayer) + random.random()*0.000000001))
+            opts.sort(key=lambda x: x[1], reverse=True)
+            print('[i] Evaluated Options:')
+            for o in opts:
+                print('[ ]' + str(o[0].lastAction) + " (Win prob: "+str(int((o[1])*10000)/100)+"%)")
+            print('[#] I choose to play: ' + str(opts[0][0].lastAction))
+            self.performAction(opts[0][0].lastAction)
+        else:
+            action = self.head.askUserForAction()
+            self.performAction(action)
+
+    def game(self, bots=None, calcDepth=7, bg=True):
+        if bg:
+            self.spawnWorker()
+        if bots == None:
+            bots = [None]*self.head.playersNum
+        while self.head.getWinner() == None:
+            self.turn(bots[self.head.curPlayer], calcDepth, bg=True)
+        print(['O', 'X', 'No one'][self.head.getWinner()] + ' won!')
+        if bg:
+            self.killWorker()
+
+    def saveModel(self, v_model, q_model, gen):
+        v_state = v_model.state_dict()
+        q_model = q_model.state_dict()
+        with open(self.getModelFileName(), 'wb') as f:
+            pickle.dump((gen, v_state, q_model), f)
+
+    def loadModelState(self, v_model, q_model):
+        with open(self.getModelFileName(), 'rb') as f:
+            gen, v_state, q_state = pickle.load(f)
+        v_model.load_state_dict(v_state)
+        q_model.load_state_dict(q_state)
+        return gen
+
+    def loadModel(self):
+        v_model, q_model = self.head.state.getVModel(), self.head.state.getQModel()
+        gen = self.loadModelState(v_model, q_model)
+        return v_model, q_model, gen
+
+    def getModelFileName(self):
+        return 'brains/uttt.vac'
+
+    def saveToMemoryBank(self, term):
+        with open('memoryBank/uttt/'+datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')+'_'+str(int(random.random()*99999))+'.vdm', 'wb') as f:
+            pickle.dump(term, f)
+
+
+class NeuralRuntime(Runtime):
+    def __init__(self, initState, **kwargs):
+        super().__init__(initState, **kwargs)
+
+        v_model, q_model, gen = self.loadModel()
+
+        self.head.universe.v_model = v_model
+        self.head.universe.q_model = q_model
+        self.head.universe.scoreProvider = 'neural'
+
+class Trainer(Runtime):
+    def __init__(self, initState, **kwargs):
+        super().__init__(initState, **kwargs)
+        self.universe = self.head.universe
+        self.rootNode = self.head
+        self.terminal = None
+
+    def buildDatasetFromModel(self, v_model, q_model, depth=4, refining=True, fanOut=[5, 5, 5, 5, 4, 4, 4, 4], uncertainSec=15, exacity=5):
+        print('[*] Building Timeline')
+        term = self.linearPlay(v_model, q_model, calcDepth=depth, exacity=exacity)
+        if refining:
+            print('[*] Refining Timeline (exploring alternative endings)')
+            cur = term
+            for d in fanOut:
+                cur = cur.parent
+                if cur == None:
+                    break
+                cur.forceStrong(d)
+                print('.', end='', flush=True)
+            print('')
+            print('[*] Refining Timeline (exploring uncertain regions)')
+            self.timelineExpandUncertain(term, uncertainSec)
+        return term
+
+    def linearPlay(self, v_model, q_model, calcDepth=7, exacity=5, verbose=False, firstNRandom=2):
+        head = self.rootNode
+        self.universe.v_model = v_model
+        self.universe.q_model = q_model
+        self.spawnWorker()
+        while head.getWinner() == None:
+            if verbose:
+                print(head)
+            else:
+                print('.', end='', flush=True)
+            head.forceStrong(calcDepth)
+            opts = []
+            if len(head.childs) == 0:
+                break
+            for c in head.childs:
+                opts.append((c, c.getStrongFor(head.curPlayer)))
+            if firstNRandom:
+                firstNRandom -= 1
+                ind = int(random.random()*len(opts))
+            else:
+                opts.sort(key=lambda x: x[1], reverse=True)
+                if exacity >= 10:
+                    ind = 0
+                else:
+                    ind = int(pow(random.random(), exacity)*(len(opts)-1))
+            head = opts[ind][0]
+        self.killWorker()
+        if verbose:
+            print(head)
+        print(' => '+['O', 'X', 'No one'][head.getWinner()] + ' won!')
+        return head
+
+    def timelineIterSingle(self, term):
+        for i in self.timelineIter(self, [term]):
+            yield i
+
+    def timelineIter(self, terms, altChildPerNode=-1):
+        batch = len(terms)
+        heads = terms
+        while True:
+            empty = True
+            for b in range(batch):
+                head = heads[b]
+                if head == None:
+                    continue
+                empty = False
+                yield head
+                if len(head.childs):
+                    if altChildPerNode == -1:  # all
+                        for child in head.childs:
+                            yield child
+                    else:
+                        for j in range(min(altChildPerNode, int(len(head.childs)/2))):
+                            yield random.choice(head.childs)
+                if head.parent == None:
+                    head = None
+                else:
+                    head = head.parent
+                heads[b] = head
+            if empty:
+                return
+
+    def timelineExpandUncertain(self, term, secs):
+        self.rootNode.universe.clearPQ()
+        self.rootNode.universe.activateEdge(self.rootNode)
+        self.spawnWorker()
+        for s in range(secs):
+            time.sleep(1)
+            print('.', end='', flush=True)
+        self.rootNode.universe.clearPQ()
+        self.killWorker()
+        print('')
+
+    def trainModel(self, v_model, q_model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, terms=None, batch=2):
+        loss_func = nn.MSELoss()
+        v_optimizer = optim.Adam(v_model.parameters(), lr)
+        q_optimizer = optim.Adam(q_model.parameters(), lr)
+        print('[*] Conditioning Brain')
+        if terms == None:
+            terms = []
+            for i in range(batch):
+                terms.append(self.buildDatasetFromModel(
+                    v_model, q_model, depth=calcDepth, exacity=exacity))
+        for r in range(16):
+            loss_sum = 0
+            lLoss = 0
+            zeroLen = 0
+            for i, node in enumerate(self.timelineIter(terms)):
+                for p in range(self.rootNode.playersNum):
+                    inp = node.state.getTensor(player=p)
+                    v = torch.tensor(
+                        [node.getStrongFor(p)], dtype=torch.float)
+                    qs = []
+                    q_preds = []
+                    q_loss = torch.Tensor([0])
+                    if node.childs:
+                        for child in node.childs:
+                            sa = child.lastAction.getTensor(node.state, player=p)
+                            q = child.getStrongFor(p)
+                            q_pred = q_model(sa)
+                            qs.append(q)
+                            q_preds.append(q_pred)
+                        qs = torch.Tensor(qs)
+                        q_target = torch.zeros_like(qs).scatter_(0, torch.argmax(qs).unsqueeze(0), 1)
+                        q_cur = torch.concat(q_preds)
+                        q_loss = loss_func(q_cur, q_target)
+                        q_optimizer.zero_grad()
+                        q_loss.backward()
+                        q_optimizer.step()
+
+                    v_pred = v_model(inp)
+                    v_loss = loss_func(v_pred, v)
+                    v_optimizer.zero_grad()
+                    v_loss.backward()
+                    v_optimizer.step()
+
+                    loss = v_loss.item() + q_loss.item()
+                    loss_sum += loss
+                    if v_loss.item() == 0.0:
+                        zeroLen += 1
+                if zeroLen == 5:
+                    break
+            print(loss_sum/i)
+            if r > 16 and (loss_sum/i < cut or lLoss == loss_sum):
+                return loss_sum
+            lLoss = loss_sum
+        return loss_sum
+
+    def main(self, v_model=None, q_model=None, gens=1024, startGen=0):
+        newModel = False
+        if v_model == None or q_model==None:
+            print('[!] No brain found. Creating new one...')
+            newModel = True
+            v_model, q_model = self.rootNode.state.getVModel(), self.rootNode.state.getQModel()
+        self.universe.scoreProvider = ['neural', 'naive'][newModel]
+        v_model.train(), q_model.train()
+        for gen in range(startGen, startGen+gens):
+            print('[#####] Gen '+str(gen)+' training:')
+            loss = self.trainModel(v_model, q_model, calcDepth=min(
+                4, 3+int(gen/16)), exacity=int(gen/3+1), batch=4)
+            print('[L] '+str(loss))
+            self.universe.scoreProvider = 'neural'
+            self.saveModel(v_model, q_model, gen)
+
+    def trainFromTerm(self, term):
+        v_model, q_model, gen = self.loadModel()
+        self.universe.scoreProvider = 'neural'
+        self.trainModel(v_model, q_model, calcDepth=4, exacity=10, term=term)
+        self.saveModel(v_model, q_model)
+
+    def train(self):
+        if os.path.exists(self.getModelFileName()):
+            v_model, q_model, gen = self.loadModel()
+            self.main(v_model, q_model, startGen=gen+1)
+        else:
+            self.main()

vacuumDecay/templates/index.html

@@ -0,0 +1,125 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Interactive Tree Visualization</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"></script>
+    <script src="https://d3js.org/d3.v5.min.js"></script>
+    <style>
+        .links line {
+            stroke: #999;
+            stroke-opacity: 0.6;
+            stroke-width: 1.5px;
+        }
+
+        .nodes rect {
+            stroke: #fff;
+            stroke-width: 1.5px;
+        }
+
+        text {
+            font: 10px sans-serif;
+            pointer-events: none;
+        }
+    </style>
+</head>
+<body>
+    <div id="graph"></div>
+    <script>
+        var socket = io();
+
+        var margin = {top: 20, right: 120, bottom: 20, left: 120},
+            width = 960 - margin.right - margin.left,
+            height = 800 - margin.top - margin.bottom;
+
+        var svg = d3.select("#graph").append("svg")
+            .attr("width", width + margin.right + margin.left)
+            .attr("height", height + margin.top + margin.bottom)
+            .append("g")
+            .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
+
+        var tree = d3.tree().size([height, width]);
+
+        var root;
+
+        socket.on('update', function(data) {
+            console.log(data);
+
+            var stratify = d3.stratify()
+                .id(function(d) { return d.id; })
+                .parentId(function(d) { return d.parentId; });
+
+            try {
+                root = stratify(data.nodes);
+            } catch (e) {
+                console.error(e);
+                return;
+            }
+
+            tree(root);
+
+            var link = svg.selectAll(".link")
+                .data(root.links(), function(d) { return d.source.id + "-" + d.target.id; });
+
+            link.exit().remove();
+
+            link.enter().append("path")
+                .attr("class", "link")
+                .merge(link)
+                .attr("d", d3.linkHorizontal()
+                    .x(function(d) { return d.y; })
+                    .y(function(d) { return d.x; }));
+
+            var node = svg.selectAll(".node")
+                .data(root.descendants(), function(d) { return d.id; });
+
+            node.exit().remove();
+
+            var nodeEnter = node.enter().append("g")
+                .attr("class", "node")
+                .attr("transform", function(d) {
+                    return "translate(" + d.y + "," + d.x + ")";
+                });
+
+            nodeEnter.append("rect")
+                .attr("width", 40)
+                .attr("height", 40)
+                .attr("x", -20)
+                .attr("y", -20)
+                .attr("fill", function(d) {
+                    var age = Date.now() - d.data.last_updated;
+                    return d3.interpolateCool(Math.min(age / 10000, 1));
+                });
+
+            nodeEnter.append("image")
+                .attr("xlink:href", function(d) { return d.data.image ? 'data:image/jpeg;base64,' + d.data.image : ''; })
+                .attr("x", -20)
+                .attr("y", -20)
+                .attr("width", 40)
+                .attr("height", 40);
+
+            nodeEnter.append("text")
+                .attr("dy", -30)
+                .attr("dx", 0)
+                .text(function(d) { return "Player: " + (d.data.currentPlayer !== undefined ? d.data.currentPlayer : 'N/A'); });
+
+            nodeEnter.append("text")
+                .attr("dy", -15)
+                .attr("dx", 0)
+                .text(function(d) {
+                    if (d.data.winProbs && d.data.winProbs.length >= 2) {
+                        return "Win Probs: P0: " + d.data.winProbs[0].toFixed(2) + ", P1: " + d.data.winProbs[1].toFixed(2);
+                    } else {
+                        return "Win Probs: N/A";
+                    }
+                });
+
+            node = nodeEnter.merge(node);
+
+            node.attr("transform", function(d) {
+                return "translate(" + d.y + "," + d.x + ")";
+            });
+        });
+    </script>
+</body>
+</html>

vacuumDecay/utils.py

@@ -0,0 +1,21 @@
+def choose(txt, options):
+    while True:
+        print('[*] '+txt)
+        for num, opt in enumerate(options):
+            print('['+str(num+1)+'] ' + str(opt))
+        inp = input('[> ')
+        try:
+            n = int(inp)
+            if n in range(1, len(options)+1):
+                return options[n-1]
+        except:
+            pass
+        for opt in options:
+            if inp == str(opt):
+                return opt
+        if len(inp) == 1:
+            for opt in options:
+                if inp == str(opt)[0]:
+                    return opt
+        print('[!] Invalid Input.')
+

vacuumDecay/visualizer.py

@@ -0,0 +1,76 @@
+import threading
+import time
+import networkx as nx
+from flask import Flask, render_template, jsonify
+from flask_socketio import SocketIO, emit
+from io import BytesIO
+import base64
+
+class Visualizer:
+    def __init__(self, runtime):
+        self.runtime = runtime
+        self.graph = nx.DiGraph()
+        self.app = Flask(__name__)
+        self.socketio = SocketIO(self.app)
+        self.init_flask()
+        self.update_thread = threading.Thread(target=self.update_periodically)
+        self.update_thread.daemon = True
+        self.update_thread.start()
+
+    def init_flask(self):
+        @self.app.route('/')
+        def index():
+            return render_template('index.html')
+
+        @self.app.route('/data')
+        def data():
+            return jsonify(self.get_data())
+
+        @self.socketio.on('connect')
+        def handle_connect():
+            print('Client connected')
+
+    def send_update(self):
+        self.socketio.emit('update', self.get_data())
+
+    def update_periodically(self):
+        while True:
+            self.send_update()
+            time.sleep(1)
+
+    def run(self):
+        self.socketio.run(self.app, debug=True, use_reloader=False)
+
+    def start(self):
+        self.thread = threading.Thread(target=self.run)
+        self.thread.start()
+
+    def get_data(self):
+        nodes_data = []
+        edges_data = []
+
+        def add_node_data(node, depth=0):
+            img = None
+            if node.state.getImage():  # depth <= 2:
+                buffered = BytesIO()
+                node.state.getImage().save(buffered, format="JPEG")
+                img = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+            nodes_data.append({
+                'id': id(node),
+                'parentId': id(node.parent) if node.parent else None,
+                'image': img,
+                'currentPlayer': node.state.curPlayer,
+                'winProbs': [node.getStrongFor(i) for i in range(node.state.playersNum)],
+                'last_updated': node.last_updated
+            })
+
+            for child in node.childs:
+                edges_data.append({'source': id(node), 'target': id(child)})
+                add_node_data(child, depth=depth + 1)
+
+        head_node = self.runtime.head
+        if head_node:
+            add_node_data(head_node)
+
+        return {'nodes': nodes_data, 'edges': edges_data}