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) @param 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) @param 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 train(self): """Train on the current dataset, for a single epoch.""" abstractMethod()
def _backwardImplementation(self, outerr, inerr, inbuf): abstractMethod()
def distributionLength(self): abstractMethod()
def getActionValues(self, state): abstractMethod()
def calculateGradient(self): abstractMethod()
def updater(self, pos, neg, poshb, neghb, posvb, negvb): abstractMethod()
def mutate(self, **args): """ Vary some properties of the underlying module, so that it's behavior changes, (but not too abruptly). """ abstractMethod()
def removeFromEnv(self, env): #Called upon removing abstractMethod()
def sampler(self, probabilities): abstractMethod()
def updateState(self, env, dt): #dt in seconds abstractMethod()
def draw(self, env): abstractMethod()
def position(self): abstractMethod()
def color(self): #Tuple of r, g, b abstractMethod()
def _initPhysics(self, *args): #Responsibility is on the superclasses to create a shape and body abstractMethod()
def __call__(self, *args, **kwargs): """ @rtype: float """ abstractMethod()
def _updateWeights(self, state, action, reward, next_state): ''' Expected to update Q-value approximator. ''' abstractMethod()
def _forwardImplementation(self, inbuf, outbuf): """Actual forward transformation function. To be overwritten in subclasses.""" abstractMethod()
def f(self, x): """ The function itself, to be defined by subclasses """ abstractMethod()
def _learnStep(self): """ The core method to be implemented by all subclasses. """ abstractMethod()
def combine(self, classifiers, input): """Receives list of trained classifers """ abstractMethod()
def produceOffspring(self): """ generate the new generation of offspring, given the current population, and their fitnesses """ abstractMethod()
def _build(self, dataset): abstractMethod()
def learn(self): """ The main method, that invokes a learning step. """ abstractMethod()
def _forwardImplementation(self, inbuf, outbuf): abstractMethod()
def _getCombinedDistribution(self, distributionMatrix, numClassifiers): abstractMethod()
def initPopulation(self): """ initialize the population """ abstractMethod()
def getDistribution(self, input): """Returns NumPy array of posterior distributions for each class.""" abstractMethod()
def randomize(self): """ Sets all variable parameters to random values. """ abstractMethod()
def isFinished(self): """ Is the current episode over? """ 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 _qValues(self, state): """ Return vector of probability of policy for all actions, given the state(-features). """ abstractMethod()
def topologyMutate(self): abstractMethod()
def learn(self): """ learn on the current dataset, for a single epoch @note: has to be implemented by all subclasses. """ 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 randomize(self): """ randomly set all variable parameters """ abstractMethod()
def updateData(self): """ overwrite this class to update whatever data the renderer needs to display the current state of the world. """ 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 _render(self): """ Here, the render methods are called. This function has to be implemented by subclasses. """ abstractMethod()
def getMaxAction(self, state): abstractMethod()
def getReward(self): """ Compute and return the current reward (i.e. corresponding to the last action performed) """ return abstractMethod()