class MinimalGameHandler:
"""
The :class:`MinimalGameHandler` class takes care of the interface to the ALE and tries to do nothing else. It's
meant for advanced users who need fine control over every aspect of the process. It has many functions that are simply
wrappers of the underlying ALE but with pythonic names/usage.
Parameters
----------
rom : byte string
Specifies the directory to load the rom from. Must be a byte string: b'dir_for_rom/rom.bin'
frame_skip : int
Default 4. Number of frames to skip inbetween action. This sets frame skip on the Stella environment
show_rom : boolean
Default False. Whether or not to show the game. True takes longer to run but can be fun to watch
"""
def __init__(self, rom, frame_skip=4, show_rom=False):
# set up emulator
self.ale = ALEInterface(show_rom)
self.ale.setInt(b'frame_skip', frame_skip)
self.ale.loadROM(rom)
# setup gamescreen object. I think this is faster than recreating an empty each time
width, height = self.ale.getScreenDims()
self.gamescreen = np.empty((height, width, 1), dtype=np.uint8)
def reset(self):
self.ale.reset_game()
def step(self, action, clip=None):
reward = 0
if clip is not None:
reward += np.clip(self.ale.act(action), 0, clip)
else:
reward += self.ale.act(action)
return reward
def get_gamescreen(self, converted=True):
self.gamescreen = self.ale.getScreenGrayscale(self.gamescreen)
if converted:
# convert ALE gamescreen into 84x84 image
processedImg = imresize(self.gamescreen[33:-16, :, 0], 0.525, interp='nearest')
return processedImg
else:
# we return a copy here because converted returns a copy also. And its easier for exp replay
return np.copy(self.gamescreen)
def get_game_over(self):
return self.ale.game_over()
def get_legal_actions(self):
return self.ale.getMinimalActionSet()
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()
# 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()
# take the action and get the reward
reward = ale.act(a)
total_reward += reward
#process pygame event queue
exit=False
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit=True
break
if(pressed[pygame.K_q]):
exit = True
if(exit):
break
#delay to 60fps
clock.tick(30.)
if(ale.game_over()):
episode_frame_number = ale.getEpisodeFrameNumber()
frame_number = ale.getFrameNumber()
print("Frame Number: " + str(frame_number) + " Episode Frame Number: " + str(episode_frame_number))
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
ale.reset_game()
total_reward = 0.0
episode = episode + 1
actions.append(copy.deepcopy(np.asarray(curractions, dtype=np.int8)))
rewards.append(copy.deepcopy(np.asarray(currrewards, dtype=np.int8)))
states.append(copy.deepcopy(np.asarray(currstates, dtype=np.bool)))
curractions.clear()
currrewards.clear()
currstates.clear()
with open(os.getcwd() + '\datasets\\spc_inv_dataset1q.pkl', 'wb') as outFile:
pickle.dump((states, actions, rewards), outFile)
class GameHandler:
"""
The :class:`GameHandler` class takes care of the interface between the ALE and the learner.
Currently supported is the ability to display the ALE screen when running, skip x number of frames by repeating the
last action, and configuring the dtype to convert the gamescreen to (default is float16 for space).
Parameters
----------
rom : byte string
Specifies the directory to load the rom from. Must be a byte string: b'dir_for_rom/rom.bin'
show_rom : boolean
Whether or not to show the game being played or not. True takes longer to run but can be fun to watch
skip_frame : int
Number of frames to skip using the last action chosen
learner : :class:`learners.learner`
Default None. The learner, on construction GameHandler will call set_legal_actions. If none then set_legal_actions
needs to be called
"""
def __init__(self, rom, show_rom, skip_frame, learner=None):
# set up emulator
self.ale = ALEInterface(show_rom)
self.ale.loadROM(rom)
(self.screen_width, self.screen_height) = self.ale.getScreenDims()
legal_actions = self.ale.getMinimalActionSet()
# set up vars
self.skipFrame = skip_frame
if learner:
learner.set_legal_actions(legal_actions)
self.total_frame_count = 0
def run_one_game(self, learner, neg_reward=False, early_return=False, clip=True, max_episode_frame=np.inf):
"""
Runs a game until ale.game_over() is true. Currently does not support stopping at a specific frame count during
an episode
Parameters
----------
learner : :class:`learners.learner`
Will call get_game_action and frames_processed.
get_game_action must return a valid ALE action ind. frames_processed can be a pass.
neg_reward : bool
Default False. Whether or not to use negative rewards, recieved when agent looses a life.
early_return : bool
Default False. If set to true and neg_rewards is set then will return on first loss of life
clip : bool
Default True. Whether or not to clip positive rewards to 1
max_episode_frame : int
Default np.inf. The maximum number of frames to run per episode
Returns
-------
int
Total reward from game. Can be negative if neg_reward is true.
"""
total_reward = 0.0
gamescreen = None
self.ale.reset_game()
cur_lives = self.ale.lives()
action_to_perform = 0 # initially set at zero because we start the game before asking the learner
while not self.ale.game_over() and self.ale.getEpisodeFrameNumber() < max_episode_frame:
# get frames
frames = list()
reward = 0
# loop over skip frame
for frame in range(self.skipFrame):
gamescreen = self.ale.getScreenGrayscale(gamescreen)
# convert ALE gamescreen into usable image, scaled between 0 and 1
processedImg = imresize(gamescreen[33:-16, :, 0], 0.525, interp="nearest")
frames.append(processedImg)
# act on the action to perform, should be ALE compatible action ind
rew = self.ale.act(action_to_perform)
# clip positive rewards to 1
if rew > 0 and clip:
reward += 1
else:
reward += rew
# if allowing negative rewards, see if lives has decreased
if neg_reward:
new_lives = self.ale.lives()
if new_lives < cur_lives:
reward -= 1 # losing a life is a negative 1 reward
cur_lives = new_lives
# end frame skip loop
total_reward += reward
frames = np.asarray(frames)
# frames_processed must be here before action_to_perform gets overwritten.
learner.frames_processed(frames, action_to_perform, reward)
action_to_perform = learner.get_game_action()
self.total_frame_count += 1 * self.skipFrame
# if doing early return, end game on first loss of life
if reward < 0 and early_return:
return total_reward
# end of game
return total_reward
def set_legal_actions(self, learner):
learner.set_legal_actions(self.ale.getMinimalActionSet())
#process pygame event queue
exit = False
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit = True
break
if (pressed[pygame.K_q]):
exit = True
if (exit):
break
#delay to 60fps
clock.tick(30.)
if (ale.game_over()):
episode_frame_number = ale.getEpisodeFrameNumber()
frame_number = ale.getFrameNumber()
print("Frame Number: " + str(frame_number) +
" Episode Frame Number: " + str(episode_frame_number))
print("Episode " + str(episode) + " ended with score: " +
str(total_reward))
ale.reset_game()
total_reward = 0.0
episode = episode + 1
actions.append(copy.deepcopy(np.asarray(curractions, dtype=np.int8)))
rewards.append(copy.deepcopy(np.asarray(currrewards, dtype=np.int8)))
states.append(copy.deepcopy(np.asarray(currstates, dtype=np.bool)))
curractions.clear()
currrewards.clear()
currstates.clear()
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()
# 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()
# take the action and get the reward
reward = ale.act(a)
total_reward += reward
frameCount += 1
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()
class GameHandler:
"""
The :class:`GameHandler` class takes care of the interface between the ALE and the learner.
Currently supported is the ability to display the ALE screen when running, skip x number of frames by repeating the
last action, and configuring the dtype to convert the gamescreen to (default is float16 for space).
Parameters
----------
rom : byte string
Specifies the directory to load the rom from. Must be a byte string: b'dir_for_rom/rom.bin'
show_rom : boolean
Whether or not to show the game being played or not. True takes longer to run but can be fun to watch
skip_frame : int
Number of frames to skip using the last action chosen
learner : :class:`learners.learner`
Default None. The learner, on construction GameHandler will call set_legal_actions. If none then set_legal_actions
needs to be called
"""
def __init__(self, rom, show_rom, skip_frame, learner=None):
# set up emulator
self.ale = ALEInterface(show_rom)
self.ale.loadROM(rom)
(self.screen_width, self.screen_height) = self.ale.getScreenDims()
legal_actions = self.ale.getMinimalActionSet()
# set up vars
self.skipFrame = skip_frame
if learner:
learner.set_legal_actions(legal_actions)
self.total_frame_count = 0
def run_one_game(self, learner, neg_reward=False, early_return=False, clip=True, max_episode_frame=np.inf):
"""
Runs a game until ale.game_over() is true.
Parameters
----------
learner : :class:`learners.learner`
Will call get_game_action and frames_processed.
get_game_action must return a valid ALE action ind. frames_processed can be a pass.
neg_reward : bool
Default False. Whether or not to use negative rewards, recieved when agent looses a life.
early_return : bool
Default False. If set to true and neg_rewards is set then will return on first loss of life
clip : bool
Default True. Whether or not to clip positive rewards to 1
max_episode_frame : int
Default np.inf. The maximum number of frames to run per episode
Returns
-------
int
Total reward from game. Can be negative if neg_reward is true.
"""
total_reward = 0.0
gamescreen = None
self.ale.reset_game()
cur_lives = self.ale.lives()
action_to_perform = 0 # initially set at zero because we start the game before asking the learner
while not self.ale.game_over() and self.ale.getEpisodeFrameNumber() < max_episode_frame:
# get frames
frames = list()
reward = 0
# loop over skip frame
for frame in range(self.skipFrame):
gamescreen = self.ale.getScreenGrayscale(gamescreen)
# convert ALE gamescreen into usable image, scaled between 0 and 1
processedImg = imresize(gamescreen[33:-16, :, 0], 0.525, interp='nearest')
frames.append(processedImg)
# act on the action to perform, should be ALE compatible action ind
rew = self.ale.act(action_to_perform)
# clip positive rewards to 1
if rew > 0 and clip:
reward += 1
else:
reward += rew
# if allowing negative rewards, see if lives has decreased
if neg_reward:
new_lives = self.ale.lives()
if new_lives < cur_lives:
reward -= 1 # losing a life is a negative 1 reward
cur_lives = new_lives
# end frame skip loop
total_reward += reward
frames = np.asarray(frames)
# frames_processed must be here before action_to_perform gets overwritten.
learner.frames_processed(frames, action_to_perform, reward)
action_to_perform = learner.get_game_action()
self.total_frame_count += 1 * self.skipFrame
# if doing early return, end game on first loss of life
if reward < 0 and early_return:
return total_reward
# end of game
return total_reward
def set_legal_actions(self, learner):
learner.set_legal_actions(self.ale.getMinimalActionSet())
def get_legal_actions(self):
return self.ale.getMinimalActionSet()
