def main():
import time
import numpy as np
from learningALE.libs.ale_python_interface import ALEInterface
# this script is used to try and find what ram index stores the number of lives for a game
ale = ALEInterface(True)
ale.loadROM(b'D:\\_code\\beam_rider.bin')
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getLegalActionSet()
frameCount = 0
ramlist = list()
st = time.time()
for episode in range(1):
total_reward = 0.0
while not ale.game_over():
a = legal_actions[np.random.randint(legal_actions.size)]
ram = ale.getRAM()
ramlist.append(ram)
reward = ale.act(a)
total_reward += reward
frameCount += 1
print("Episode " + str(episode) + " ended with score: " +
str(total_reward))
ale.reset_game()
et = time.time()
print(et - st, frameCount / (et - st))
import matplotlib.pyplot as plt
ramarray = np.asarray(ramlist)
w = np.where(ramarray > 3)[1]
ramarray[:, w] = 0
plt.plot(ramarray)
notZ = np.where(ramarray != 0)[1]
unqNZ = np.unique(notZ)
print(unqNZ)
def main():
import time
import numpy as np
from learningALE.libs.ale_python_interface import ALEInterface
# this script is used to try and find what ram index stores the number of lives for a game
ale = ALEInterface(True)
ale.loadROM(b'D:\\_code\\beam_rider.bin')
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getLegalActionSet()
frameCount = 0
ramlist = list()
st = time.time()
for episode in range(1):
total_reward = 0.0
while not ale.game_over():
a = legal_actions[np.random.randint(legal_actions.size)]
ram = ale.getRAM()
ramlist.append(ram)
reward = ale.act(a)
total_reward += reward
frameCount += 1
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
ale.reset_game()
et = time.time()
print(et-st, frameCount/(et-st))
import matplotlib.pyplot as plt
ramarray = np.asarray(ramlist)
w = np.where(ramarray > 3)[1]
ramarray[:, w] = 0
plt.plot(ramarray)
notZ = np.where(ramarray != 0)[1]
unqNZ = np.unique(notZ)
print(unqNZ)
def main():
import os
import pickle
import time
import lasagne
import matplotlib.pyplot as plt
import numpy as np
from learners.nns import AlloEggoCnn
from scipy.misc import imresize
from learningALE.handlers import ActionHandler, ActionPolicy
from learningALE.libs.ale_python_interface import ALEInterface
dtype = np.float16
plt.ion()
# set up emulator
ale = ALEInterface(True)
ale.loadROM(b'D:\\_code\\breakout.bin')
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getMinimalActionSet()
lives = 5
# set up vars
skipFrame = 4
actionHandler = ActionHandler(ActionPolicy.eGreedy, (0.1, 0.1, 2),
legal_actions)
scoreList = list()
cnn = AlloEggoCnn()
with open(os.getcwd() + '\saves\cnnbestalloego.pkl', 'rb') as fin:
parms = pickle.load(fin)
lasagne.layers.set_all_param_values(cnn.a_out, parms)
frameCount = 0
st = time.time()
for episode in range(100):
total_reward = 0.0
while not ale.game_over():
# get frames
frames = list()
reward = 0
for frame in range(skipFrame):
gamescreen = ale.getScreenRGB()
processedImg = np.asarray(imresize(gamescreen.view(
np.uint8).reshape(screen_height, screen_width, 4)[:, :, 0],
0.5,
interp='nearest'),
dtype=dtype) / 255
frames.append(processedImg)
performedAction, actionInd = actionHandler.getLastAction()
rew = ale.act(performedAction)
if rew > 0:
reward += 1
ram = ale.getRAM()
if ram[57] != lives:
reward -= 1
lives = ram[57]
frames = np.asarray(frames)
actionVect = cnn.get_output(frames.reshape(
(1, skipFrame, 105, 80)))[0]
actionHandler.setAction(actionVect)
total_reward += reward
frameCount += 1 * skipFrame
ale.reset_game()
actionHandler.anneal()
scoreList.append(total_reward)
lives = 5
print("Episode " + str(episode) + " ended with score: " +
str(total_reward))
et = time.time()
print('Total Time:', et - st, 'Frame Count:', frameCount, 'FPS:',
frameCount / (et - st))
plt.clf()
plt.plot(scoreList, '.')
plt.pause(0.01)
plt.ioff()
frameCount = 0
st = time.time()
for episode in range(1):
total_reward = 0.0
while not ale.game_over():
# get a random action
a = legal_actions[np.random.randint(legal_actions.size)]
# get gamescreen and convert to usable format (Height x Width x Channels)
gamescreen = ale.getScreenRGB()
gamescreen = np.asarray(gamescreen.view(np.uint8).reshape(
screen_height, screen_width, 4)[:, :, 0],
dtype=np.float)
# get ram
ram = ale.getRAM()
# take the action and get the reward
reward = ale.act(a)
total_reward += reward
frameCount += 1
print("Episode " + str(episode) + " ended with score: " +
str(total_reward))
# game over man game over, reset
ale.reset_game()
# end time count and print total time and FPS
et = time.time()
print(et - st, frameCount / (et - st))
reward += ale.act(a)
total_reward += reward
currrewards.append(reward)
currstates.append(imresize(np.asarray(frames), 0.525, interp='nearest'))
frames = np.swapaxes(np.asarray(frames),0,2)
from pygame import surfarray
frames = surfarray.make_surface(frames)
screen.blit(pygame.transform.scale(frames, (screen_width*2, screen_height*2)),(0,0))
#get RAM
ram_size = ale.getRAMSize()
ram = np.zeros((ram_size),dtype=np.uint8)
ale.getRAM(ram)
#Display ram bytes
font = pygame.font.SysFont("Ubuntu Mono",32)
text = font.render("RAM: " ,1,(255,208,208))
screen.blit(text,(330,10))
font = pygame.font.SysFont("Ubuntu Mono",25)
height = font.get_height()*1.2
line_pos = 40
ram_pos = 0
while(ram_pos < 128):
ram_string = ''.join(["%02X "%ram[x] for x in range(ram_pos,min(ram_pos+16,128))])
text = font.render(ram_string,1,(255,255,255))
currrewards.append(reward)
currstates.append(imresize(np.asarray(frames), 0.525, interp='nearest'))
frames = np.swapaxes(np.asarray(frames), 0, 2)
from pygame import surfarray
frames = surfarray.make_surface(frames)
screen.blit(
pygame.transform.scale(frames, (screen_width * 2, screen_height * 2)),
(0, 0))
#get RAM
ram_size = ale.getRAMSize()
ram = np.zeros((ram_size), dtype=np.uint8)
ale.getRAM(ram)
#Display ram bytes
font = pygame.font.SysFont("Ubuntu Mono", 32)
text = font.render("RAM: ", 1, (255, 208, 208))
screen.blit(text, (330, 10))
font = pygame.font.SysFont("Ubuntu Mono", 25)
height = font.get_height() * 1.2
line_pos = 40
ram_pos = 0
while (ram_pos < 128):
ram_string = ''.join(
["%02X " % ram[x] for x in range(ram_pos, min(ram_pos + 16, 128))])
text = font.render(ram_string, 1, (255, 255, 255))
def main():
import os
import pickle
import time
import lasagne
import matplotlib.pyplot as plt
import numpy as np
from learners.nns import AlloEggoCnn
from scipy.misc import imresize
from learningALE.handlers import ActionHandler, ActionPolicy
from learningALE.libs.ale_python_interface import ALEInterface
dtype = np.float16
plt.ion()
# set up emulator
ale = ALEInterface(True)
ale.loadROM(b'D:\\_code\\breakout.bin')
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getMinimalActionSet()
lives = 5
# set up vars
skipFrame = 4
actionHandler = ActionHandler(ActionPolicy.eGreedy, (0.1, 0.1, 2), legal_actions)
scoreList = list()
cnn = AlloEggoCnn()
with open(os.getcwd() + '\saves\cnnbestalloego.pkl', 'rb') as fin:
parms = pickle.load(fin)
lasagne.layers.set_all_param_values(cnn.a_out, parms)
frameCount = 0
st = time.time()
for episode in range(100):
total_reward = 0.0
while not ale.game_over():
# get frames
frames = list()
reward = 0
for frame in range(skipFrame):
gamescreen = ale.getScreenRGB()
processedImg = np.asarray(
imresize(gamescreen.view(np.uint8).reshape(screen_height, screen_width, 4)[:, :, 0], 0.5, interp='nearest'),
dtype=dtype)/255
frames.append(processedImg)
performedAction, actionInd = actionHandler.getLastAction()
rew = ale.act(performedAction)
if rew > 0:
reward += 1
ram = ale.getRAM()
if ram[57] != lives:
reward -= 1
lives = ram[57]
frames = np.asarray(frames)
actionVect = cnn.get_output(frames.reshape((1, skipFrame, 105, 80)))[0]
actionHandler.setAction(actionVect)
total_reward += reward
frameCount += 1*skipFrame
ale.reset_game()
actionHandler.anneal()
scoreList.append(total_reward)
lives = 5
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
et = time.time()
print('Total Time:', et-st, 'Frame Count:', frameCount, 'FPS:',frameCount/(et-st))
plt.clf()
plt.plot(scoreList, '.')
plt.pause(0.01)
plt.ioff()
legal_actions = ale.getLegalActionSet()
frameCount = 0
st = time.time()
for episode in range(1):
total_reward = 0.0
while not ale.game_over():
# get a random action
a = legal_actions[np.random.randint(legal_actions.size)]
# get gamescreen and convert to usable format (Height x Width x Channels)
gamescreen = ale.getScreenRGB()
gamescreen = np.asarray(gamescreen.view(np.uint8).reshape(screen_height, screen_width, 4)[:, :, 0], dtype=np.float)
# get ram
ram = ale.getRAM()
# take the action and get the reward
reward = ale.act(a)
total_reward += reward
frameCount += 1
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
# game over man game over, reset
ale.reset_game()
# end time count and print total time and FPS
et = time.time()
print(et-st, frameCount/(et-st))
