def get_player(directory=None,
files_list=None,
landmark_ids=None,
viz=False,
task="play",
file_type="brain",
saveGif=False,
saveVideo=False,
multiscale=True,
history_length=20,
agents=1,
logger=None):
env = MedicalPlayer(directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
file_type=file_type,
landmark_ids=landmark_ids,
history_length=history_length,
multiscale=multiscale,
agents=agents,
logger=logger)
if task != "train":
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a
# queue
env = FrameStack(env, FRAME_HISTORY, agents)
return env
def get_player(directory=None,
files_list=None,
viz=False,
task='play',
saveGif=False,
saveVideo=False,
agents=2,
reward_strategy=1):
# in atari paper, max_num_frames = 30000
env = MedicalPlayer(directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
agents=agents,
max_num_frames=1500,
reward_strategy=reward_strategy)
if (task != 'train'):
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a queue
env = FrameStack(env, FRAME_HISTORY, agents=agents)
return env
def get_player(
directory=None,
files_list=None,
viz=False,
task="play",
saveGif=False,
saveVideo=False,
agents=2,
fiducials=None,
infDir="../inference",
):
# in atari paper, max_num_frames = 30000
env = MedicalPlayer(
directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
agents=agents,
max_num_frames=1500,
fiducials=fiducials,
infDir=infDir,
)
if task != "train":
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a queue
env = FrameStack(env, FRAME_HISTORY, agents=agents)
return env