def generator(self, data_dir, tmp_dir):
env_spec = lambda: atari_wrappers.wrap_atari( # pylint: disable=g-long-lambda
gym.make(self.env_name),
warp=False,
frame_skip=4,
frame_stack=False)
hparams = rl.atari_base()
with tf.variable_scope("train", reuse=tf.AUTO_REUSE):
policy_lambda = hparams.network
policy_factory = tf.make_template(
"network",
functools.partial(policy_lambda, env_spec().action_space, hparams))
self._max_frame_pl = tf.placeholder(
tf.float32, self.env.observation_space.shape)
actor_critic = policy_factory(tf.expand_dims(tf.expand_dims(
self._max_frame_pl, 0), 0))
policy = actor_critic.policy
self._last_policy_op = policy.mode()
with tf.Session() as sess:
model_saver = tf.train.Saver(
tf.global_variables(".*network_parameters.*"))
model_saver.restore(sess, FLAGS.model_path)
for item in super(GymPongTrajectoriesFromPolicy,
self).generator(data_dir, tmp_dir):
yield item
def __init__(self, event_dir, *args, **kwargs):
super(GymPongTrajectoriesFromPolicy, self).__init__(*args, **kwargs)
self._env = None
self._event_dir = event_dir
env_spec = lambda: atari_wrappers.wrap_atari( # pylint: disable=g-long-lambda
gym.make("PongNoFrameskip-v4"),
warp=False,
frame_skip=4,
frame_stack=False)
hparams = rl.atari_base()
with tf.variable_scope("train"):
policy_lambda = hparams.network
policy_factory = tf.make_template(
"network",
functools.partial(policy_lambda,
env_spec().action_space, hparams))
self._max_frame_pl = tf.placeholder(
tf.float32, self.env.observation_space.shape)
actor_critic = policy_factory(
tf.expand_dims(tf.expand_dims(self._max_frame_pl, 0), 0))
policy = actor_critic.policy
self._last_policy_op = policy.mode()
self._last_action = self.env.action_space.sample()
self._skip = 4
self._skip_step = 0
self._obs_buffer = np.zeros((2, ) + self.env.observation_space.shape,
dtype=np.uint8)
self._sess = tf.Session()
model_saver = tf.train.Saver(
tf.global_variables(".*network_parameters.*"))
model_saver.restore(self._sess, FLAGS.model_path)
def __init__(self, *args, **kwargs):
super(GymDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
self._env = None
self.debug_dump_frames_path = "debug_frames_env"
# defaults
self.environment_spec = lambda: gym.make("PongDeterministic-v4")
self.in_graph_wrappers = []
self.collect_hparams = rl.atari_base()
self.settable_num_steps = 20000
self.simulated_environment = None
self.warm_up = 10
def __init__(self, *args, **kwargs):
super(GymDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
self._env = None
self.history_size = 2
# defaults
self.environment_spec = lambda: gym.make("PongDeterministic-v4")
self.in_graph_wrappers = [(atari.MaxAndSkipWrapper, {"skip": 4})]
self.collect_hparams = rl.atari_base()
self.settable_num_steps = 1000
self.simulated_environment = None
self.warm_up = 70
def __init__(self, *args, **kwargs):
super(GymDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
self._env = None
self.debug_dump_frames_path = "debug_frames_env"
self.make_extra_debug_info = False
# defaults
self.environment_spec = lambda: gym.make(self.env_name)
self.in_graph_wrappers = []
self.collect_hparams = rl.atari_base()
self.settable_num_steps = 20000
self.simulated_environment = None
self.warm_up = 10 # TODO(piotrm): This should be probably removed.
def __init__(self, *args, **kwargs):
super(GymDiscreteProblem, self).__init__(*args, **kwargs)
self.num_channels = 3
self.history_size = 2
# defaults
self.environment_spec = lambda: gym.make("PongNoFrameskip-v4")
self.in_graph_wrappers = [(MaxAndSkipWrapper, {"skip": 4})]
self.collect_hparams = rl.atari_base()
self.num_steps = 1000
self.movies = True
self.movies_fps = 24
self.simulated_environment = None
self.warm_up = 70