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testing_universe.py
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testing_universe.py
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# CS630: Machine Learning Final Project
# Lior Hirschfeld, Jihoun Im, Kevin Sun, and Henry Desai
# IMPORTS
import gym
import universe # register the universe environments
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from IPython.display import clear_output
import random
import numpy as np
import pickle
from skimage.color import rgb2gray
from skimage.transform import resize
from keras.models import load_model
# HYPERPARAMATERS
DEATH_COST = -1/600 # Sets how much should the bot be harmed for dying.
LOAD = False # Sets whether a previous model should be loaded.
PLAY_AFTER = True # Sets whether the model plays after it finishes training.
BATCH_SIZE = 10 # Sets the batch size for catastrophic forgetting defence.
LEARNING_RATE=0.0001 # Sets the speed at which the model learns.
EQUALIZE = False # Sets whether the model should EQUALIZE rewards.
epochs = 3 # Sets the number of games the model trains on.
gamma = 0.9 # Sets the amount the model consider future reward.
epsilon = 1 # Sets exploration vs exploitation value.
# HELPER METHODS
def makeMove(state, action):
# This method takes an action and moves the mouse accordingly.
mousePositions = []
for i in range(8):
mousePositions.append((100 * np.cos(2 * np.pi / 8 * i), 100 * np.sin(2 * np.pi / 8 * i)))
if action < 8:
action_n = [[('PointerEvent', mousePositions[int(action)//2][0] + 265, mousePositions[int(action)//2][1] + 235, False)]]
else:
action_n = [[('PointerEvent', mousePositions[int(action)//2][0] + 265, mousePositions[int(action)//2][1] + 235, True)]]
return env.step(action_n)
def simplify(data):
# This method simplifies the data received from Universe to something managable.
data = np.array(data)[0:530,0:470,0:3] # Ignore all but the game screen.
data = rgb2gray(data) # Collapse RGB
data = resize(data, (53, 47)) # BIN
return np.array(data)
if not LOAD:
# Construct a new model.
# The size an # of hidden layers can be messed with here.
# We found that these numbers worked moderately well on the GPU.
model = Sequential()
model.add(Dense(2000, init='uniform', input_shape=(53*47,)))
model.add(Activation('tanh'))
model.add(Dense(16, init='uniform'))
model.add(Activation('linear'))
rms = RMSprop()
model.compile(loss='mse', optimizer=RMSprop(lr=LEARNING_RATE))
else:
# Load an existing, trained model.
model = load_model('model250.h5')
# Store data recording the success of each model run in a list.
try:
pelletsEarnedList = pickle.load(open('pelletsearned.p', 'rb'))
pelletsEarnedList.append([])
except:
pelletsEarnedList = [[]]
# INITIALIZE UNIVERSE ENVIRONMENT
games = 0
done_n = [False]
env = gym.make('internet.SlitherIO-v0')
env.configure(remotes=1) # automatically creates a local docker container
state = env.reset()
while games < epochs:
pelletsEarned = 0 # Keeps track of # of pellets earned in this epoch.
rounds = 0 # Records the number of frames the model has learned.
replay = [] # Stores a list of old state, rewards, and actions
buff = 80 # Sets the max length of replay.
h = 0
while True: #Wait for Universe to finish initializing.
action_n = [[('PointerEvent', 200, 200, False)]]
state, reward_n, done_n, info = env.step(action_n)
env.render()
try:
state[0]['vision']
break
except:
pass
state = simplify(state[0]['vision'])
while not done_n[0]: # While the game is still in progress.
# Store expected value for all possible actions.
qval = model.predict(state.reshape(1,53*47), batch_size=1)
# Take a random or the predicted best action.
if (random.random() < epsilon):
action = np.random.randint(0,16)
else:
action = (np.argmax(qval))
#Take an action and observe the new state.
new_state, reward, done_n, info = makeMove(state, action)
pelletsEarned += reward[0]
if done_n[0]:
new_state = state
reward[0] = None
else:
new_state = simplify(new_state[0]['vision'])
# Rerender the screen. This can be skipped if the
# developer doesn't care about watching the snake.
env.render()
if (len(replay) < buff):
# While the buffer is being filled, don't train.
replay.append((state, action, reward, new_state))
else:
# Now, take a random selection of replay and train off of that.
if (h < (buff - 1)):
h += 1
else:
h = 0
replay[h] = (state, action, reward, new_state) # Add current situation to replays.
minibatch = random.sample(replay, BATCH_SIZE)
X_train = []
Y_train = []
for memory in minibatch:
old_state, action, reward, new_state = memory
oldQ = model.predict(old_state.reshape(1,53*47), batch_size=1)
newQ = model.predict(new_state.reshape(1,53*47), batch_size=1)
maxQ = np.max(newQ)
y = np.zeros((1,16))
y[:] = oldQ[:]
# Calculate how the reward should be updated.
if reward[0]: # non-terminal state
update = ((reward[0]) + (gamma * maxQ))
if action >= 8:
update -= 0.1 #Penalize the model for boosting
else: # terminal state
update = (DEATH_COST * rounds + (gamma * maxQ))
y[0][action] = update
X_train.append(old_state.reshape(53*47))
Y_train.append(y.reshape(16,))
X_train = np.array(X_train)
Y_train = np.array(Y_train)
model.fit(X_train, Y_train, batch_size=BATCH_SIZE, nb_epoch=1, verbose=0)
state = new_state
clear_output(wait=True)
rounds += 1
# Record the number of pellents earned in this epoch.
# NOTE: This does not reflect the final score of the snake
# as it does not consider how many of the pellets earned
# were lost in boosting.
pelletsEarnedList[len(pelletsEarnedList)-1].append(pelletsEarned)
if epsilon > 0.1:
# Now that the model has trained, make it more certain in the future.
epsilon -= (1/(epochs))
games += 1
if games % 10 == 0:
# Every 10 runs, resave the model (in case of internet loss).
model.save('model250.h5')
pickle.dump(pelletsEarnedList, open('pelletsearned.p', 'wb'))
pickle.dump(pelletsEarnedList, open('pelletsearned.p', 'wb'))
while PLAY_AFTER:
while True: # Wait for Universe to finish initializing.
action_n = [[('PointerEvent', 200, 200, False)]]
state, reward_n, done_n, info = env.step(action_n)
env.render()
try:
state[0]['vision']
break
except:
pass
state = simplify(state[0]['vision'])
if EQUALIZE:
# When we first started running our model, we noticed that it would
# always prefer one direction after it had finished training.
# Hypothetically, on average, all directional movements should be
# equally likely, so we established this method.
# By calculating the average reward given to each action,
# which should hypothetically be the same (for 0-7) in a proper model,
# and then subtracting that from all future predictions, we can get
# an idea of how 'relatively' good the model thinks the action is in
# any state.
averages = np.zeros((1, 16))
i = 0
while(not done_n[0] and i < 100):
i += 1
qval = model.predict(state.reshape(1,53*47), batch_size=1)
action = (np.argmax(qval))
new_state, reward, done_n, info = makeMove(state, action)
averages += qval
averages /= 100
while(not done_n[0]):
qval = model.predict(state.reshape(1,53*47), batch_size=1)
if EQUALIZE:
action = np.argmax(qval - averages)
else:
action = np.argmax(qval)
new_state, reward, done_n, info = makeMove(state, action)
if done_n[0]:
new_state = state
else:
new_state = simplify(new_state[0]['vision'])
env.render()
state = new_state
clear_output(wait=True)