def getSensors(self):
""" the currently visible state of the world (the observation may be
stochastic - repeated calls returning different values)
:rtype: by default, this is assumed to be a numpy array of doubles
:note: This function is abstract and has to be implemented.
"""
abstractMethod()
def performAction(self, action):
""" perform an action on the world that changes it's internal state (maybe stochastically)
:key action: an action that should be executed in the Environment, by an agent.
:type action: tuple: (agentID, action value)
:note: This function is abstract and has to be implemented.
"""
abstractMethod()
def performAction(self, action):
""" perform an action on the world that changes it's internal state (maybe
stochastically).
:key action: an action that should be executed in the Environment.
:type action: by default, this is assumed to be a numpy array of doubles
:note: This function is abstract and has to be implemented.
"""
abstractMethod()
def _learnStep(self):
""" The core method to be implemented by all subclasses. """
abstractMethod()
def getMaxAction(self, state):
abstractMethod()
def isFinished(self):
""" Is the current episode over? """
abstractMethod()
def updater(self, pos, neg, poshb, neghb, posvb, negvb):
abstractMethod()
def _forwardImplementation(self, inbuf, outbuf):
"""Actual forward transformation function. To be overwritten in
subclasses."""
abstractMethod()
def learn(self):
""" The main method, that invokes a learning step. """
abstractMethod()
def f(self, x):
""" The function itself, to be defined by subclasses """
abstractMethod()
def _render(self):
""" Here, the render methods are called. This function has to be implemented by subclasses. """
abstractMethod()
def updateData(self):
""" overwrite this class to update whatever data the renderer needs to display the current
state of the world. """
abstractMethod()
def mutate(self, **args):
""" Vary some properties of the underlying module, so that it's behavior
changes, (but not too abruptly). """
abstractMethod()
def randomize(self):
""" Sets all variable parameters to random values. """
abstractMethod()
def getActionValues(self, state):
abstractMethod()
def topologyMutate(self):
abstractMethod()
def train(self):
"""Train on the current dataset, for a single epoch."""
abstractMethod()
def _forwardImplementation(self, inbuf, outbuf):
abstractMethod()
def doMove(self, player, action):
""" the core method to be implemented bu all TwoPlayerGames:
what happens when a player performs an action. """
abstractMethod()
def _backwardImplementation(self, outerr, inerr, inbuf):
abstractMethod()
def sampler(self, probabilities):
abstractMethod()
def initPopulation(self):
""" initialize the population """
abstractMethod()
def getAction(self):
""" Return a chosen action.
:rtype: by default, this is assumed to ba a numpy array of doubles.
:note: This method is abstract and needs to be implemented.
"""
abstractMethod()
def produceOffspring(self):
""" generate the new generation of offspring, given the current population, and their fitnesses """
abstractMethod()
def getReward(self):
""" Compute and return the current reward (i.e. corresponding to the last action performed) """
return abstractMethod()
def calculateGradient(self):
abstractMethod()
