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)
from learningALE.libs.ale_python_interface import ALEInterface
"""
This example is meant for those wanting to play around with GameHandler, or implement their own ALE interface.
For people that want a plug and play interface use :class:`handlers.GameHandler`
"""
# start up the python ale interface
ale = ALEInterface()
# load a rom
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)
"""
This example is meant for those wanting to play around with GameHandler, or implement their own ALE interface.
For people that want a plug and play interface use :class:`handlers.GameHandler`
"""
# start up the python ale interface
ale = ALEInterface()
# load a rom
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()
