deepq.py

# import os
# os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32"
# import theano

# import the neural net stuff
from keras.models import Sequential
from keras import optimizers
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.regularizers import l2

# import other stuff
import random
import numpy as np

from memory import Memory

class DeepQ:
    def __init__(self, inputs, outputs, memorySize, discountFactor, learningRate, learnStart):
        self.input_size = inputs
        self.output_size = outputs
        self.memory = Memory(memorySize)
        self.discountFactor = discountFactor
        self.learnStart = learnStart
        self.learningRate = learningRate
   
    def initNetworks(self, hiddenLayers):
        model = self.createModel(self.input_size, self.output_size, hiddenLayers, "relu", self.learningRate)
        self.model = model

        targetModel = self.createModel(self.input_size, self.output_size, hiddenLayers, "relu", self.learningRate)
        self.targetModel = targetModel

    def createRegularizedModel(self, inputs, outputs, hiddenLayers, activationType, learningRate):
        bias = True
        dropout = 0
        regularizationFactor = 0.01
        model = Sequential()
        if len(hiddenLayers) == 0: 
            model.add(Dense(self.output_size, input_shape=(self.input_size,), init='lecun_uniform', bias=bias))
            model.add(Activation("linear"))
        else :
            if regularizationFactor > 0:
                model.add(Dense(hiddenLayers[0], input_shape=(self.input_size,), init='lecun_uniform', W_regularizer=l2(regularizationFactor),  bias=bias))
            else:
                model.add(Dense(hiddenLayers[0], input_shape=(self.input_size,), init='lecun_uniform', bias=bias))

            if (activationType == "LeakyReLU") :
                model.add(LeakyReLU(alpha=0.01))
            else :
                model.add(Activation(activationType))
            
            for index in range(1, len(hiddenLayers)-1):
                layerSize = hiddenLayers[index]
                if regularizationFactor > 0:
                    model.add(Dense(layerSize, init='lecun_uniform', W_regularizer=l2(regularizationFactor), bias=bias))
                else:
                    model.add(Dense(layerSize, init='lecun_uniform', bias=bias))
                if (activationType == "LeakyReLU") :
                    model.add(LeakyReLU(alpha=0.01))
                else :
                    model.add(Activation(activationType))
                if dropout > 0:
                    model.add(Dropout(dropout))
            model.add(Dense(self.output_size, init='lecun_uniform', bias=bias))
            model.add(Activation("linear"))
        optimizer = optimizers.RMSprop(lr=learningRate, rho=0.9, epsilon=1e-06)
        model.compile(loss="mse", optimizer=optimizer)
        return model

    def createModel(self, inputs, outputs, hiddenLayers, activationType, learningRate):
        model = Sequential()
        if len(hiddenLayers) == 0: 
            model.add(Dense(self.output_size, input_shape=(self.input_size,), init='lecun_uniform'))
            model.add(Activation("linear"))
        else :
            model.add(Dense(hiddenLayers[0], input_shape=(self.input_size,), init='lecun_uniform'))
            if (activationType == "LeakyReLU") :
                model.add(LeakyReLU(alpha=0.01))
            else :
                model.add(Activation(activationType))
            
            for index in range(1, len(hiddenLayers)-1):
                layerSize = hiddenLayers[index]
                model.add(Dense(layerSize, init='lecun_uniform'))
                if (activationType == "LeakyReLU") :
                    model.add(LeakyReLU(alpha=0.01))
                else :
                    model.add(Activation(activationType))
            model.add(Dense(self.output_size, init='lecun_uniform'))
            model.add(Activation("linear"))
        optimizer = optimizers.RMSprop(lr=learningRate, rho=0.9, epsilon=1e-06)
        model.compile(loss="mse", optimizer=optimizer)
        return model

    def printNetwork(self):
        i = 0
        for layer in self.model.layers:
            weights = layer.get_weights()
            print "layer ",i,": ",weights
            i += 1


    def backupNetwork(self, model, backup):
        weightMatrix = []
        for layer in model.layers:
            weights = layer.get_weights()
            weightMatrix.append(weights)
        i = 0
        for layer in backup.layers:
            weights = weightMatrix[i]
            layer.set_weights(weights)
            i += 1

    def updateTargetNetwork(self):
        self.backupNetwork(self.model, self.targetModel)

    # predict Q values for all the actions
    def getQValues(self, state):
        predicted = self.model.predict(state.reshape(1,len(state)))
        return predicted[0]

    def getTargetQValues(self, state):
        predicted = self.targetModel.predict(state.reshape(1,len(state)))
        return predicted[0]

    def getMaxQ(self, qValues):
        return np.max(qValues)

    def getMaxIndex(self, qValues):
        return np.argmax(qValues)

    # calculate the target function
    def calculateTarget(self, qValuesNewState, reward, isFinal):
        if isFinal:
            return reward
        else : 
            return reward + self.discountFactor * self.getMaxQ(qValuesNewState)

    # select the action with the highest Q value
    def selectAction(self, qValues, explorationRate):
        rand = random.random()
        if rand < explorationRate :
            action = np.random.randint(0, self.output_size)
        else :
            action = self.getMaxIndex(qValues)
        return action

    def selectActionByProbability(self, qValues, bias):
        qValueSum = 0
        shiftBy = 0
        for value in qValues:
            if value + shiftBy < 0:
                shiftBy = - (value + shiftBy)
        shiftBy += 1e-06

        for value in qValues:
            qValueSum += (value + shiftBy) ** bias

        probabilitySum = 0
        qValueProbabilities = []
        for value in qValues:
            probability = ((value + shiftBy) ** bias) / float(qValueSum)
            qValueProbabilities.append(probability + probabilitySum)
            probabilitySum += probability
        qValueProbabilities[len(qValueProbabilities) - 1] = 1

        rand = random.random()
        i = 0
        for value in qValueProbabilities:
            if (rand <= value):
                return i
            i += 1

    def addMemory(self, state, action, reward, newState, isFinal):
        self.memory.addMemory(state, action, reward, newState, isFinal)

    def learnOnLastState(self):
        if self.memory.getCurrentSize() >= 1:
            return self.memory.getMemory(self.memory.getCurrentSize() - 1)

    def learnOnMiniBatch(self, miniBatchSize, useTargetNetwork=True):
        if self.memory.getCurrentSize() > self.learnStart :
            miniBatch = self.memory.getMiniBatch(miniBatchSize)
            X_batch = np.empty((0,self.input_size), dtype = np.float64)
            Y_batch = np.empty((0,self.output_size), dtype = np.float64)
            for sample in miniBatch:
                isFinal = sample['isFinal']
                state = sample['state']
                action = sample['action']
                reward = sample['reward']
                newState = sample['newState']

                qValues = self.getQValues(state)
                if useTargetNetwork:
                    qValuesNewState = self.getTargetQValues(newState)
                else :
                    qValuesNewState = self.getQValues(newState)
                targetValue = self.calculateTarget(qValuesNewState, reward, isFinal)

                X_batch = np.append(X_batch, np.array([state.copy()]), axis=0)
                Y_sample = qValues.copy()
                Y_sample[action] = targetValue
                Y_batch = np.append(Y_batch, np.array([Y_sample]), axis=0)
            self.model.fit(X_batch, Y_batch, batch_size = len(miniBatch), nb_epoch=1, verbose = 0)

lunar_runnner.py

# import the gym stuff
import gym
# import other stuff
import random
import numpy as np
# import own classes
from deepq import DeepQ

print(gym.envs.registry.all())
env = gym.make('LunarLander-v1')

epochs = 10000
steps = 1000
updateTargetNetwork = 10000
explorationRate = 1
minibatch_size = 32
learnStart = 32
learningRate = 0.00025
discountFactor = 0.99
memorySize = 1000000

last100Scores = [0] * 100
last100ScoresIndex = 0
last100Filled = False

renderPerXEpochs = 50
shouldRender = True

deepQ = DeepQ(len(env.observation_space.high), env.action_space.n, memorySize, discountFactor, learningRate, learnStart)
deepQ.initNetworks([30,30,30])

stepCounter = 0

# number of reruns
for epoch in xrange(epochs):
    observation = env.reset()
    print explorationRate
    # number of timesteps
    totalReward = 0
    for t in xrange(steps):
        if epoch % renderPerXEpochs == 0 and shouldRender:
            env.render()
        qValues = deepQ.getQValues(observation)

        action = deepQ.selectAction(qValues, explorationRate)

        newObservation, reward, done, info = env.step(action)

        totalReward += reward

        deepQ.addMemory(observation, action, reward, newObservation, done)

        if stepCounter >= learnStart:
            if stepCounter <= updateTargetNetwork:
                deepQ.learnOnMiniBatch(minibatch_size, False)
            else :
                deepQ.learnOnMiniBatch(minibatch_size, True)

        observation = newObservation

        if done:
            last100Scores[last100ScoresIndex] = totalReward
            last100ScoresIndex += 1
            if last100ScoresIndex >= 100:
                last100Filled = True
                last100ScoresIndex = 0
            if not last100Filled:
                print "Episode ",epoch," finished after {} timesteps".format(t+1)," with total reward",totalReward
            else :
                print "Episode ",epoch," finished after {} timesteps".format(t+1)," with total reward",totalReward," last 100 average: ",(sum(last100Scores)/len(last100Scores))
            break

        stepCounter += 1
        if stepCounter % updateTargetNetwork == 0:
            deepQ.updateTargetNetwork()
            print "updating target network"

    explorationRate *= 0.995
    explorationRate = max (0.05, explorationRate)

memory.py

import numpy as np
import random

class Memory:
    def __init__(self, size):
        self.size = size
        self.currentPosition = 0
        self.states = []
        self.actions = []
        self.rewards = []
        self.newStates = []
        self.finals = []

    def getMiniBatch(self, size) :
        indices = random.sample(np.arange(len(self.states)), min(size,len(self.states)) )
        miniBatch = []
        for index in indices:
            miniBatch.append({'state': self.states[index],'action': self.actions[index], 'reward': self.rewards[index], 'newState': self.newStates[index], 'isFinal': self.finals[index]})
        return miniBatch

    def getCurrentSize(self) :
        return len(self.states)

    def getMemory(self, index): 
        return {'state': self.states[index],'action': self.actions[index], 'reward': self.rewards[index], 'newState': self.newStates[index], 'isFinal': self.finals[index]}

    def addMemory(self, state, action, reward, newState, isFinal) :
        if (self.currentPosition >= self.size - 1) :
            self.currentPosition = 0
        if (len(self.states) > self.size) :
            self.states[self.currentPosition] = state
            self.actions[self.currentPosition] = action
            self.rewards[self.currentPosition] = reward
            self.newStates[self.currentPosition] = newState
            self.finals[self.currentPosition] = isFinal
        else :
            self.states.append(state)
            self.actions.append(action)
            self.rewards.append(reward)
            self.newStates.append(newState)
            self.finals.append(isFinal)
        
        self.currentPosition += 1

I used the same Q learning algorithm as I used to solve the cartpole problem. (https://gym.openai.com/evaluations/eval_nI8cryNQaKlFKv592N7hQ)
Only this time with more iterations and a smaller minibatch size for training.