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()
ale.loadROM(b'd:\_code\_reinforcementlearning\\breakout.bin')
# screen dimensions and legal actions
(screen_width, screen_height) = ale.getScreenDims()
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))
def main():
import os
import pickle
import matplotlib.pyplot as plt
from learningALE.learners.nns import CNN
from scipy.misc import imresize
from learningALE.handlers.actionhandler import ActionHandler, ActionPolicy
from learningALE.libs.ale_python_interface import ALEInterface
import lasagne
import numpy as np
# plt.ion()
skipFrame = 3
cnn = CNN((None, skipFrame, 86, 80), 6, .1, stride=(4,2))
with open(os.getcwd()+'\datasets\\spccnn.pkl', 'rb') as infile:
parms = pickle.load(infile)
lasagne.layers.set_all_param_values(cnn.l_out, parms)
# rom = b'D:\\_code\\breakout.bin'
rom = b'D:\\_code\\space_invaders.bin'
ale = ALEInterface(True)
ale.loadROM(rom)
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getMinimalActionSet()
# get labels
labels = ['noop', 'fire', 'up', 'right', 'left', 'down', 'upright', 'upleft', 'downright', 'downleft', 'upfire', 'rightfire', 'leftfire', 'downfire', 'uprightfire'
, 'upleftfire', 'downrightfire', 'downleftfire']
labels = np.asarray(labels)[legal_actions]
# set up vars
actionHandler = ActionHandler(ActionPolicy.eGreedy, (.1, .1, 2), legal_actions)
rewList = list()
for ep in range(100):
total_reward = 0.0
trainCount = 0
ale.reset_game()
while not ale.game_over():
# get frames
frames = list()
reward = 0
for frame in range(skipFrame):
gamescreen = ale.getScreenRGB()
processedImg = np.asarray(
gamescreen.view(np.uint8).reshape(screen_height, screen_width, 4)[25:-12, :, 0],
dtype=np.float32)
processedImg[processedImg > 1] = 255
processedImg = imresize(processedImg, 0.5, interp='nearest')/255
frames.append(processedImg)
performedAction, actionInd = actionHandler.getLastAction()
rew = ale.act(performedAction)
if rew > 0:
rew = 1
reward += rew
total_reward += reward
frames = np.asarray(frames, dtype=np.float32)
actionVect = cnn.get_output(frames.reshape((1, skipFrame, frames.shape[1], 80)))[0]
actionHandler.setAction(actionVect)
# hid1_act = cnn.get_hid1_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# hid2_act = cnn.get_hid2_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# for x in range(hid1_act.shape[1]):
# plt.subplot(4,4,x+1)
# plt.imshow(hid1_act[0,x], cmap=plt.cm.gray)
# for x in range(hid2_act.shape[1]):
# plt.subplot(6,6,x+1)
# plt.imshow(hid2_act[0,x], cmap=plt.cm.gray)
# plt.show()
# plt.clf()
# plt.plot(actionVect)
# plt.xticks(range(len(labels)), labels)
# plt.pause(0.001)
rewList.append(total_reward)
print(ep, total_reward)
print(np.mean(rewList), np.std(rewList), np.max(rewList), np.min(rewList))
print(np.unique(rewList, return_counts=True))
plt.plot(rewList)
plt.show()
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()
ale.loadROM(b'd:\_code\_reinforcementlearning\\breakout.bin')
# screen dimensions and legal actions
(screen_width, screen_height) = ale.getScreenDims()
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()
def main():
import os
import pickle
import matplotlib.pyplot as plt
from learningALE.learners.nns import CNN
from scipy.misc import imresize
from learningALE.handlers.actionhandler import ActionHandler, ActionPolicy
from learningALE.libs.ale_python_interface import ALEInterface
import lasagne
import numpy as np
# plt.ion()
skipFrame = 3
cnn = CNN((None, skipFrame, 86, 80), 6, .1, stride=(4, 2))
with open(os.getcwd() + '\datasets\\spccnn.pkl', 'rb') as infile:
parms = pickle.load(infile)
lasagne.layers.set_all_param_values(cnn.l_out, parms)
# rom = b'D:\\_code\\breakout.bin'
rom = b'D:\\_code\\space_invaders.bin'
ale = ALEInterface(True)
ale.loadROM(rom)
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getMinimalActionSet()
# get labels
labels = [
'noop', 'fire', 'up', 'right', 'left', 'down', 'upright', 'upleft',
'downright', 'downleft', 'upfire', 'rightfire', 'leftfire', 'downfire',
'uprightfire', 'upleftfire', 'downrightfire', 'downleftfire'
]
labels = np.asarray(labels)[legal_actions]
# set up vars
actionHandler = ActionHandler(ActionPolicy.eGreedy, (.1, .1, 2),
legal_actions)
rewList = list()
for ep in range(100):
total_reward = 0.0
trainCount = 0
ale.reset_game()
while not ale.game_over():
# get frames
frames = list()
reward = 0
for frame in range(skipFrame):
gamescreen = ale.getScreenRGB()
processedImg = np.asarray(gamescreen.view(np.uint8).reshape(
screen_height, screen_width, 4)[25:-12, :, 0],
dtype=np.float32)
processedImg[processedImg > 1] = 255
processedImg = imresize(processedImg, 0.5,
interp='nearest') / 255
frames.append(processedImg)
performedAction, actionInd = actionHandler.getLastAction()
rew = ale.act(performedAction)
if rew > 0:
rew = 1
reward += rew
total_reward += reward
frames = np.asarray(frames, dtype=np.float32)
actionVect = cnn.get_output(
frames.reshape((1, skipFrame, frames.shape[1], 80)))[0]
actionHandler.setAction(actionVect)
# hid1_act = cnn.get_hid1_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# hid2_act = cnn.get_hid2_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# for x in range(hid1_act.shape[1]):
# plt.subplot(4,4,x+1)
# plt.imshow(hid1_act[0,x], cmap=plt.cm.gray)
# for x in range(hid2_act.shape[1]):
# plt.subplot(6,6,x+1)
# plt.imshow(hid2_act[0,x], cmap=plt.cm.gray)
# plt.show()
# plt.clf()
# plt.plot(actionVect)
# plt.xticks(range(len(labels)), labels)
# plt.pause(0.001)
rewList.append(total_reward)
print(ep, total_reward)
print(np.mean(rewList), np.std(rewList), np.max(rewList), np.min(rewList))
print(np.unique(rewList, return_counts=True))
plt.plot(rewList)
plt.show()
