def __init__(self, obs_space, action_space, config):
self.observation_space = obs_space
self.action_space = action_space
self.action_noise_std = config["action_noise_std"]
self.preprocessor = ModelCatalog.get_preprocessor_for_space(
self.observation_space)
self.observation_filter = get_filter(config["observation_filter"],
self.preprocessor.shape)
self.single_threaded = config.get("single_threaded", False)
self.sess = make_session(single_threaded=self.single_threaded)
self.inputs = tf.placeholder(tf.float32,
[None] + list(self.preprocessor.shape))
# Policy network.
dist_class, dist_dim = ModelCatalog.get_action_dist(
self.action_space, config["model"], dist_type="deterministic")
model = ModelCatalog.get_model({SampleBatch.CUR_OBS: self.inputs},
self.observation_space,
self.action_space, dist_dim,
config["model"])
dist = dist_class(model.outputs, model)
self.sampler = dist.sample()
self.variables = ray.experimental.tf_utils.TensorFlowVariables(
model.outputs, self.sess)
self.num_params = sum(
np.prod(variable.shape.as_list())
for _, variable in self.variables.variables.items())
self.sess.run(tf.global_variables_initializer())
def __init__(self, obs_space, action_space, config):
super().__init__(obs_space, action_space, config)
self.action_noise_std = self.config["action_noise_std"]
self.preprocessor = ModelCatalog.get_preprocessor_for_space(
self.observation_space)
self.observation_filter = get_filter(self.config["observation_filter"],
self.preprocessor.shape)
self.single_threaded = self.config.get("single_threaded", False)
if self.config["framework"] == "tf":
self.sess = make_session(single_threaded=self.single_threaded)
# Set graph-level seed.
if config.get("seed") is not None:
with self.sess.as_default():
tf1.set_random_seed(config["seed"])
self.inputs = tf1.placeholder(tf.float32, [None] +
list(self.preprocessor.shape))
else:
if not tf1.executing_eagerly():
tf1.enable_eager_execution()
self.sess = self.inputs = None
if config.get("seed") is not None:
# Tf2.x.
if config.get("framework") == "tf2":
tf.random.set_seed(config["seed"])
# Tf-eager.
elif tf1 and config.get("framework") == "tfe":
tf1.set_random_seed(config["seed"])
# Policy network.
self.dist_class, dist_dim = ModelCatalog.get_action_dist(
self.action_space, self.config["model"], dist_type="deterministic")
self.model = ModelCatalog.get_model_v2(
obs_space=self.preprocessor.observation_space,
action_space=self.action_space,
num_outputs=dist_dim,
model_config=self.config["model"],
)
self.sampler = None
if self.sess:
dist_inputs, _ = self.model({SampleBatch.CUR_OBS: self.inputs})
dist = self.dist_class(dist_inputs, self.model)
self.sampler = dist.sample()
self.variables = ray.experimental.tf_utils.TensorFlowVariables(
dist_inputs, self.sess)
self.sess.run(tf1.global_variables_initializer())
else:
self.variables = ray.experimental.tf_utils.TensorFlowVariables(
[], None, self.model.variables())
self.num_params = sum(
np.prod(variable.shape.as_list())
for _, variable in self.variables.variables.items())
