def get_player(viz=False, train=False):
env = AtariPlayer(ROM_FILE, frame_skip=ACTION_REPEAT, viz=viz,
live_lost_as_eoe=train, max_num_frames=30000)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
if not train:
# in training, history is taken care of in expreplay buffer
env = FrameStack(env, FRAME_HISTORY)
return env
def get_player(train=False, dumpdir=None):
env = gym.make(ENV_NAME)
if dumpdir:
env = gym.wrappers.Monitor(env, dumpdir)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
env = FrameStack(env, 4)
if train:
env = LimitLength(env, 60000)
return env
def get_player(rom, viz=False, train=False):
env = AtariPlayer(rom,
frame_skip=ACTION_REPEAT,
viz=viz,
live_lost_as_eoe=train,
max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
if not train:
# in training, context is taken care of in expreplay buffer
env = FrameStack(env, CONTEXT_LEN)
return env
def get_player(viz=False, train=False):
if USE_GYM:
env = gym.make(ENV_NAME)
else:
from atari import AtariPlayer
env = AtariPlayer(ENV_NAME, frame_skip=4, viz=viz,
live_lost_as_eoe=train, max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: resize_keepdims(im, IMAGE_SIZE))
if not train:
# in training, history is taken care of in expreplay buffer
env = FrameStack(env, FRAME_HISTORY)
if train and USE_GYM:
env = LimitLength(env, 60000)
return env
def get_player(rom,
image_size,
viz=False,
train=False,
frame_skip=1,
context_len=1):
env = AtariPlayer(rom,
frame_skip=frame_skip,
viz=viz,
live_lost_as_eoe=train,
max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, image_size))
if not train:
# in training, context is taken care of in expreplay buffer
env = FrameStack(env, context_len)
return env
def get_player(train=False, dumpdir=None):
use_gym = not ENV_NAME.endswith(".bin")
if use_gym:
env = gym.make(ENV_NAME)
else:
from atari import AtariPlayer
env = AtariPlayer(ENV_NAME,
frame_skip=4,
viz=False,
live_lost_as_eoe=train,
max_num_frames=60000,
grayscale=False)
if dumpdir:
env = gym.wrappers.Monitor(env, dumpdir, video_callable=lambda _: True)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
env = FrameStack(env, 4)
if train and use_gym:
env = LimitLength(env, 60000)
return env
self.model.fit(ob_train, target_f, epochs=1, verbose=0)
self.teaching = False
self.frustration = self.frustration_max
self.has_taught = 0
if self.epsilon > self.min_epsilon:
self.epsilon *= self.epsilon_decay
if __name__ == "__main__":
ENV_NAME = 'MsPacman-v0'
NUM_ACTIONS = get_player().action_space.n
env = gym.make(ENV_NAME)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, (84,84)))
env = FrameStack(env, 4)
pred = OfflinePredictor(PredictConfig(
model=Model(),
session_init=get_model_loader("models/MsPacman-v0.tfmodel"),
input_names=['state'],
output_names=['policy']))
student = student_dqn(env, teacher=pred)
episodes = 1000000
scores = []
teacher_step_nums = []
step_nums = []
ep_avgs = [0]
ep_avg = []
