#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)
#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:
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())
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()
