def _init(self, inputs, num_outputs, options):
x = inputs
with tf.name_scope("convnet"):
for i in range(4):
x = tf.nn.elu(conv2d(x, 32, "l{}".format(i+1), [3, 3], [2, 2]))
r, c = x.shape[1].value, x.shape[2].value
x = tf.reshape(x, [-1, r*c*32])
fc1 = linear(x, 256, "fc1")
fc2 = linear(x, num_outputs, "fc2", normc_initializer(0.01))
return fc2, fc1
def value_function(self):
assert self.cur_instance, "must call forward first"
with self._branch_variable_scope("value_function"):
# Simple case: sharing the feature layer
if self.model_config["vf_share_layers"]:
return tf.reshape(
linear(self.cur_instance.last_layer, 1,
"value_function", normc_initializer(1.0)), [-1])
# Create a new separate model with no RNN state, etc.
branch_model_config = self.model_config.copy()
branch_model_config["free_log_std"] = False
if branch_model_config["use_lstm"]:
branch_model_config["use_lstm"] = False
logger.warning(
"It is not recommended to use a LSTM model with "
"vf_share_layers=False (consider setting it to True). "
"If you want to not share layers, you can implement "
"a custom LSTM model that overrides the "
"value_function() method.")
branch_instance = self.legacy_model_cls(
self.cur_instance.input_dict,
self.obs_space,
self.action_space,
1,
branch_model_config,
state_in=None,
seq_lens=None)
return tf.reshape(branch_instance.outputs, [-1])
def vf_template(last_layer, input_dict):
with tf.variable_scope(self.variable_scope):
with tf.variable_scope("value_function"):
# Simple case: sharing the feature layer
if model_config["vf_share_layers"]:
return tf.reshape(
linear(last_layer, 1, "value_function",
normc_initializer(1.0)), [-1])
# Create a new separate model with no RNN state, etc.
branch_model_config = model_config.copy()
branch_model_config["free_log_std"] = False
if branch_model_config["use_lstm"]:
branch_model_config["use_lstm"] = False
logger.warning(
"It is not recommended to use a LSTM model "
"with vf_share_layers=False (consider "
"setting it to True). If you want to not "
"share layers, you can implement a custom "
"LSTM model that overrides the "
"value_function() method.")
branch_instance = legacy_model_cls(
input_dict,
obs_space,
action_space,
1,
branch_model_config,
state_in=None,
seq_lens=None)
return tf.reshape(branch_instance.outputs, [-1])
def _setup_graph(self, ob_space, ac_space):
self.x = tf.placeholder(tf.float32, [None] + list(ob_space.shape))
dist_class, self.logit_dim = ModelCatalog.get_action_dist(
ac_space, self.config["model"])
self._model = LSTM(self.x, self.logit_dim, {})
self.state_in = self._model.state_in
self.state_out = self._model.state_out
self.logits = self._model.outputs
self.action_dist = dist_class(self.logits)
# with tf.variable_scope("vf"):
# vf_model = ModelCatalog.get_model(self.x, 1)
self.vf = tf.reshape(
linear(self._model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.sample = self.action_dist.sample()
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
self.global_step = tf.get_variable("global_step", [],
tf.int32,
initializer=tf.constant_initializer(
0, dtype=tf.int32),
trainable=False)
def __init__(self, observation_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
self.sess = tf.get_default_session()
# Setup the policy
self.observations = tf.placeholder(
tf.float32, [None] + list(observation_space.shape))
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_model(self.observations, logit_dim,
self.config["model"])
action_dist = dist_class(self.model.outputs)
self.vf = tf.reshape(
linear(self.model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
# Setup the policy loss
if isinstance(action_space, gym.spaces.Box):
ac_size = action_space.shape[0]
actions = tf.placeholder(tf.float32, [None, ac_size], name="ac")
elif isinstance(action_space, gym.spaces.Discrete):
actions = tf.placeholder(tf.int64, [None], name="ac")
else:
raise UnsupportedSpaceException(
"Action space {} is not supported for A3C.".format(
action_space))
advantages = tf.placeholder(tf.float32, [None], name="advantages")
v_target = tf.placeholder(tf.float32, [None], name="v_target")
self.loss = A3CLoss(action_dist, actions, advantages, v_target,
self.vf, self.config["vf_loss_coeff"],
self.config["entropy_coeff"])
# Initialize TFPolicyGraph
loss_in = [
("obs", self.observations),
("actions", actions),
("advantages", advantages),
("value_targets", v_target),
]
TFPolicyGraph.__init__(
self,
observation_space,
action_space,
self.sess,
obs_input=self.observations,
action_sampler=action_dist.sample(),
loss=self.loss.total_loss,
loss_inputs=loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])
self.sess.run(tf.global_variables_initializer())
def value_function(self):
"""Builds the value function output.
This method can be overridden to customize the implementation of the
value function (e.g., not sharing hidden layers).
Returns:
Tensor of size [BATCH_SIZE] for the value function.
"""
return tf.reshape(
linear(self.last_layer, 1, "value", normc_initializer(1.0)), [-1])
def value_function(self):
"""Builds the value function output.
This method can be overridden to customize the implementation of the
value function (e.g., not sharing hidden layers).
Returns:
Tensor of size [BATCH_SIZE] for the value function.
"""
return tf.reshape(
linear(self.last_layer, 1, "value", normc_initializer(1.0)), [-1])
def _build_layers_v2(self, input_dict, num_outputs, options):
cell_size = options.get("lstm_cell_size")
if options.get("lstm_use_prev_action_reward"):
action_dim = int(
np.product(
input_dict["prev_actions"].get_shape().as_list()[1:]))
features = tf.concat(
[
input_dict["obs"],
tf.reshape(
tf.cast(input_dict["prev_actions"], tf.float32),
[-1, action_dim]),
tf.reshape(input_dict["prev_rewards"], [-1, 1]),
],
axis=1)
else:
features = input_dict["obs"]
last_layer = add_time_dimension(features, self.seq_lens)
# Setup the LSTM cell
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(
tf.float32, [None, lstm.state_size.c], name="c")
h_in = tf.placeholder(
tf.float32, [None, lstm.state_size.h], name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(
lstm,
last_layer,
initial_state=state_in,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.state_out = list(lstm_state)
# Compute outputs
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def _build_layers_v2(self, input_dict, num_outputs, options):
cell_size = options.get("lstm_cell_size")
if options.get("lstm_use_prev_action_reward"):
action_dim = int(
np.product(
input_dict["prev_actions"].get_shape().as_list()[1:]))
features = tf.concat(
[
input_dict["obs"],
tf.reshape(
tf.cast(input_dict["prev_actions"], tf.float32),
[-1, action_dim]),
tf.reshape(input_dict["prev_rewards"], [-1, 1]),
],
axis=1)
else:
features = input_dict["obs"]
last_layer = add_time_dimension(features, self.seq_lens)
# Setup the LSTM cell
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(
tf.float32, [None, lstm.state_size.c], name="c")
h_in = tf.placeholder(
tf.float32, [None, lstm.state_size.h], name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(
lstm,
last_layer,
initial_state=state_in,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.state_out = list(lstm_state)
# Compute outputs
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def _setup_graph(self, ob_space, ac_space):
self.x = tf.placeholder(tf.float32, [None] + list(ob_space))
dist_class, self.logit_dim = ModelCatalog.get_action_dist(ac_space)
self._model = ModelCatalog.get_model(
self.registry, self.x, self.logit_dim, self.config["model"])
self.logits = self._model.outputs
self.curr_dist = dist_class(self.logits)
self.vf = tf.reshape(linear(self._model.last_layer, 1, "value",
normc_initializer(1.0)), [-1])
self.sample = self.curr_dist.sample()
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
self.global_step = tf.get_variable(
"global_step", [], tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
def _build_layers(self, inputs, num_outputs, options):
cell_size = options.get("lstm_cell_size", 256)
use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
distutils.version.LooseVersion("1.0.0"))
last_layer = add_time_dimension(inputs, self.seq_lens)
# Setup the LSTM cell
if use_tf100_api:
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
else:
lstm = rnn.rnn_cell.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(tf.float32, [None, lstm.state_size.c],
name="c")
h_in = tf.placeholder(tf.float32, [None, lstm.state_size.h],
name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
if use_tf100_api:
state_in = rnn.LSTMStateTuple(c_in, h_in)
else:
state_in = rnn.rnn_cell.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(lstm,
last_layer,
initial_state=state_in,
sequence_length=self.seq_lens,
time_major=False)
self.state_out = list(lstm_state)
# Compute outputs
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def _init(self, inputs, num_outputs, options):
use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
distutils.version.LooseVersion("1.0.0"))
self.x = x = inputs
for i in range(4):
x = tf.nn.elu(conv2d(x, 32, "l{}".format(i + 1), [3, 3], [2, 2]))
# Introduce a "fake" batch dimension of 1 after flatten so that we can
# do LSTM over the time dim.
x = tf.expand_dims(flatten(x), [0])
size = 256
if use_tf100_api:
lstm = rnn.BasicLSTMCell(size, state_is_tuple=True)
else:
lstm = rnn.rnn_cell.BasicLSTMCell(size, state_is_tuple=True)
step_size = tf.shape(self.x)[:1]
c_init = np.zeros((1, lstm.state_size.c), np.float32)
h_init = np.zeros((1, lstm.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h])
self.state_in = [c_in, h_in]
if use_tf100_api:
state_in = rnn.LSTMStateTuple(c_in, h_in)
else:
state_in = rnn.rnn_cell.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(lstm,
x,
initial_state=state_in,
sequence_length=step_size,
time_major=False)
lstm_c, lstm_h = lstm_state
x = tf.reshape(lstm_out, [-1, size])
logits = linear(x, num_outputs, "action", normc_initializer(0.01))
self.state_out = [lstm_c[:1, :], lstm_h[:1, :]]
return logits, x
def _init(self, inputs, num_outputs, options):
use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
distutils.version.LooseVersion("1.0.0"))
self.x = x = inputs
for i in range(4):
x = tf.nn.elu(conv2d(x, 32, "l{}".format(i + 1), [3, 3], [2, 2]))
# Introduce a "fake" batch dimension of 1 after flatten so that we can
# do LSTM over the time dim.
x = tf.expand_dims(flatten(x), [0])
size = 256
if use_tf100_api:
lstm = rnn.BasicLSTMCell(size, state_is_tuple=True)
else:
lstm = rnn.rnn_cell.BasicLSTMCell(size, state_is_tuple=True)
step_size = tf.shape(self.x)[:1]
c_init = np.zeros((1, lstm.state_size.c), np.float32)
h_init = np.zeros((1, lstm.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h])
self.state_in = [c_in, h_in]
if use_tf100_api:
state_in = rnn.LSTMStateTuple(c_in, h_in)
else:
state_in = rnn.rnn_cell.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(lstm, x,
initial_state=state_in,
sequence_length=step_size,
time_major=False)
lstm_c, lstm_h = lstm_state
x = tf.reshape(lstm_out, [-1, size])
logits = linear(x, num_outputs, "action", normc_initializer(0.01))
self.state_out = [lstm_c[:1, :], lstm_h[:1, :]]
return logits, x
def _build_layers(self, inputs, num_outputs, options):
cell_size = options.get("lstm_cell_size", 256)
last_layer = add_time_dimension(inputs, self.seq_lens)
# Setup the LSTM cell
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(
tf.float32, [None, lstm.state_size.c], name="c")
h_in = tf.placeholder(
tf.float32, [None, lstm.state_size.h], name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
lstm_out, lstm_state = tf.nn.dynamic_rnn(
lstm,
last_layer,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.state_out = list(lstm_state)
# Compute outputs
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def __init__(self, observation_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
self.sess = tf.get_default_session()
# Setup the policy
self.observations = tf.placeholder(
tf.float32, [None] + list(observation_space.shape))
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
prev_actions = ModelCatalog.get_action_placeholder(action_space)
prev_rewards = tf.placeholder(tf.float32, [None], name="prev_reward")
self.model = ModelCatalog.get_model({
"obs": self.observations,
"prev_actions": prev_actions,
"prev_rewards": prev_rewards
}, observation_space, logit_dim, self.config["model"])
action_dist = dist_class(self.model.outputs)
self.vf = tf.reshape(
linear(self.model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
# Setup the policy loss
if isinstance(action_space, gym.spaces.Box):
ac_size = action_space.shape[0]
actions = tf.placeholder(tf.float32, [None, ac_size], name="ac")
elif isinstance(action_space, gym.spaces.Discrete):
actions = tf.placeholder(tf.int64, [None], name="ac")
else:
raise UnsupportedSpaceException(
"Action space {} is not supported for A3C.".format(
action_space))
advantages = tf.placeholder(tf.float32, [None], name="advantages")
self.v_target = tf.placeholder(tf.float32, [None], name="v_target")
self.loss = A3CLoss(action_dist, actions, advantages, self.v_target,
self.vf, self.config["vf_loss_coeff"],
self.config["entropy_coeff"])
# Initialize TFPolicyGraph
loss_in = [
("obs", self.observations),
("actions", actions),
("prev_actions", prev_actions),
("prev_rewards", prev_rewards),
("advantages", advantages),
("value_targets", self.v_target),
]
LearningRateSchedule.__init__(self, self.config["lr"],
self.config["lr_schedule"])
TFPolicyGraph.__init__(
self,
observation_space,
action_space,
self.sess,
obs_input=self.observations,
action_sampler=action_dist.sample(),
loss=self.loss.total_loss,
loss_inputs=loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
prev_action_input=prev_actions,
prev_reward_input=prev_rewards,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])
self.stats_fetches = {
"stats": {
"cur_lr": tf.cast(self.cur_lr, tf.float64),
"policy_loss": self.loss.pi_loss,
"policy_entropy": self.loss.entropy,
"grad_gnorm": tf.global_norm(self._grads),
"var_gnorm": tf.global_norm(self.var_list),
"vf_loss": self.loss.vf_loss,
"vf_explained_var": explained_variance(self.v_target, self.vf),
},
}
self.sess.run(tf.global_variables_initializer())
def __init__(self,
observation_space,
action_space,
config,
existing_inputs=None):
"""
Arguments:
observation_space: Environment observation space specification.
action_space: Environment action space specification.
config (dict): Configuration values for PPO graph.
existing_inputs (list): Optional list of tuples that specify the
placeholders upon which the graph should be built upon.
"""
config = dict(ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG, **config)
self.sess = tf.get_default_session()
self.action_space = action_space
self.config = config
self.kl_coeff_val = self.config["kl_coeff"]
self.kl_target = self.config["kl_target"]
dist_cls, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
if existing_inputs:
obs_ph, value_targets_ph, adv_ph, act_ph, \
logits_ph, vf_preds_ph = existing_inputs[:6]
existing_state_in = existing_inputs[6:-1]
existing_seq_lens = existing_inputs[-1]
else:
obs_ph = tf.placeholder(tf.float32,
name="obs",
shape=(None, ) + observation_space.shape)
adv_ph = tf.placeholder(tf.float32,
name="advantages",
shape=(None, ))
act_ph = ModelCatalog.get_action_placeholder(action_space)
logits_ph = tf.placeholder(tf.float32,
name="logits",
shape=(None, logit_dim))
vf_preds_ph = tf.placeholder(tf.float32,
name="vf_preds",
shape=(None, ))
value_targets_ph = tf.placeholder(tf.float32,
name="value_targets",
shape=(None, ))
existing_state_in = None
existing_seq_lens = None
self.observations = obs_ph
self.loss_in = [
("obs", obs_ph),
("value_targets", value_targets_ph),
("advantages", adv_ph),
("actions", act_ph),
("logits", logits_ph),
("vf_preds", vf_preds_ph),
]
self.model = ModelCatalog.get_model(obs_ph,
logit_dim,
self.config["model"],
state_in=existing_state_in,
seq_lens=existing_seq_lens)
# KL Coefficient
self.kl_coeff = tf.get_variable(initializer=tf.constant_initializer(
self.kl_coeff_val),
name="kl_coeff",
shape=(),
trainable=False,
dtype=tf.float32)
self.logits = self.model.outputs
curr_action_dist = dist_cls(self.logits)
self.sampler = curr_action_dist.sample()
if self.config["use_gae"]:
if self.config["vf_share_layers"]:
self.value_function = tf.reshape(
linear(self.model.last_layer, 1, "value",
normc_initializer(1.0)), [-1])
else:
vf_config = self.config["model"].copy()
# Do not split the last layer of the value function into
# mean parameters and standard deviation parameters and
# do not make the standard deviations free variables.
vf_config["free_log_std"] = False
vf_config["use_lstm"] = False
with tf.variable_scope("value_function"):
self.value_function = ModelCatalog.get_model(
obs_ph, 1, vf_config).outputs
self.value_function = tf.reshape(self.value_function, [-1])
else:
self.value_function = tf.zeros(shape=tf.shape(obs_ph)[:1])
if self.model.state_in:
max_seq_len = tf.reduce_max(self.model.seq_lens)
mask = tf.sequence_mask(self.model.seq_lens, max_seq_len)
mask = tf.reshape(mask, [-1])
else:
mask = tf.ones_like(adv_ph)
self.loss_obj = PPOLoss(action_space,
value_targets_ph,
adv_ph,
act_ph,
logits_ph,
vf_preds_ph,
curr_action_dist,
self.value_function,
self.kl_coeff,
mask,
entropy_coeff=self.config["entropy_coeff"],
clip_param=self.config["clip_param"],
vf_clip_param=self.config["vf_clip_param"],
vf_loss_coeff=self.config["vf_loss_coeff"],
use_gae=self.config["use_gae"])
LearningRateSchedule.__init__(self, self.config["lr"],
self.config["lr_schedule"])
TFPolicyGraph.__init__(self,
observation_space,
action_space,
self.sess,
obs_input=obs_ph,
action_sampler=self.sampler,
loss=self.loss_obj.loss,
loss_inputs=self.loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=config["model"]["max_seq_len"])
self.sess.run(tf.global_variables_initializer())
self.explained_variance = explained_variance(value_targets_ph,
self.value_function)
self.stats_fetches = {
"cur_lr": tf.cast(self.cur_lr, tf.float64),
"total_loss": self.loss_obj.loss,
"policy_loss": self.loss_obj.mean_policy_loss,
"vf_loss": self.loss_obj.mean_vf_loss,
"vf_explained_var": self.explained_variance,
"kl": self.loss_obj.mean_kl,
"entropy": self.loss_obj.mean_entropy
}
def __init__(self,
observation_space,
action_space,
config,
existing_inputs=None):
config = dict(ray.rllib.agents.impala.impala.DEFAULT_CONFIG, **config)
assert config["batch_mode"] == "truncate_episodes", \
"Must use `truncate_episodes` batch mode with V-trace."
self.config = config
self.sess = tf.get_default_session()
# Create input placeholders
if existing_inputs:
actions, dones, behaviour_logits, rewards, observations = \
existing_inputs[:5]
existing_state_in = existing_inputs[5:-1]
existing_seq_lens = existing_inputs[-1]
else:
if isinstance(action_space, gym.spaces.Discrete):
ac_size = action_space.n
actions = tf.placeholder(tf.int64, [None], name="ac")
else:
raise UnsupportedSpaceException(
"Action space {} is not supported for IMPALA.".format(
action_space))
dones = tf.placeholder(tf.bool, [None], name="dones")
rewards = tf.placeholder(tf.float32, [None], name="rewards")
behaviour_logits = tf.placeholder(
tf.float32, [None, ac_size], name="behaviour_logits")
observations = tf.placeholder(
tf.float32, [None] + list(observation_space.shape))
existing_state_in = None
existing_seq_lens = None
# Setup the policy
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_model(
observations,
logit_dim,
self.config["model"],
state_in=existing_state_in,
seq_lens=existing_seq_lens)
action_dist = dist_class(self.model.outputs)
values = tf.reshape(
linear(self.model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
def to_batches(tensor):
if self.config["model"]["use_lstm"]:
B = tf.shape(self.model.seq_lens)[0]
T = tf.shape(tensor)[0] // B
else:
# Important: chop the tensor into batches at known episode cut
# boundaries. TODO(ekl) this is kind of a hack
T = self.config["sample_batch_size"]
B = tf.shape(tensor)[0] // T
rs = tf.reshape(tensor,
tf.concat([[B, T], tf.shape(tensor)[1:]], axis=0))
# swap B and T axes
return tf.transpose(
rs,
[1, 0] + list(range(2, 1 + int(tf.shape(tensor).shape[0]))))
if self.model.state_in:
max_seq_len = tf.reduce_max(self.model.seq_lens) - 1
mask = tf.sequence_mask(self.model.seq_lens, max_seq_len)
mask = tf.reshape(mask, [-1])
else:
mask = tf.ones_like(rewards)
# Inputs are reshaped from [B * T] => [T - 1, B] for V-trace calc.
self.loss = VTraceLoss(
actions=to_batches(actions)[:-1],
actions_logp=to_batches(action_dist.logp(actions))[:-1],
actions_entropy=to_batches(action_dist.entropy())[:-1],
dones=to_batches(dones)[:-1],
behaviour_logits=to_batches(behaviour_logits)[:-1],
target_logits=to_batches(self.model.outputs)[:-1],
discount=config["gamma"],
rewards=to_batches(rewards)[:-1],
values=to_batches(values)[:-1],
bootstrap_value=to_batches(values)[-1],
valid_mask=to_batches(mask)[:-1],
vf_loss_coeff=self.config["vf_loss_coeff"],
entropy_coeff=self.config["entropy_coeff"],
clip_rho_threshold=self.config["vtrace_clip_rho_threshold"],
clip_pg_rho_threshold=self.config["vtrace_clip_pg_rho_threshold"])
# Initialize TFPolicyGraph
loss_in = [
("actions", actions),
("dones", dones),
("behaviour_logits", behaviour_logits),
("rewards", rewards),
("obs", observations),
]
LearningRateSchedule.__init__(self, self.config["lr"],
self.config["lr_schedule"])
TFPolicyGraph.__init__(
self,
observation_space,
action_space,
self.sess,
obs_input=observations,
action_sampler=action_dist.sample(),
loss=self.loss.total_loss,
loss_inputs=loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])
self.sess.run(tf.global_variables_initializer())
self.stats_fetches = {
"stats": {
"cur_lr": tf.cast(self.cur_lr, tf.float64),
"policy_loss": self.loss.pi_loss,
"entropy": self.loss.entropy,
"grad_gnorm": tf.global_norm(self._grads),
"var_gnorm": tf.global_norm(self.var_list),
"vf_loss": self.loss.vf_loss,
"vf_explained_var": explained_variance(
tf.reshape(self.loss.vtrace_returns.vs, [-1]),
tf.reshape(to_batches(values)[:-1], [-1])),
},
}
def __init__(self,
observation_space,
action_space,
config,
existing_inputs=None,
unsupType='action',
designHead='universe'):
"""
Arguments:
observation_space: Environment observation space specification.
action_space: Environment action space specification.
config (dict): Configuration values for PPO graph.
existing_inputs (list): Optional list of tuples that specify the
placeholders upon which the graph should be built upon.
"""
self.unsup = unsupType is not None
# self.cur_batch = None
# self.cur_sample_batch = {}
predictor = None
numaction = action_space.n
config = dict(ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG, **config)
self.sess = tf.get_default_session()
self.action_space = action_space
self.config = config
self.kl_coeff_val = self.config["kl_coeff"]
self.kl_target = self.config["kl_target"]
dist_cls, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
if existing_inputs:
obs_ph, value_targets_ph, adv_ph, act_ph, \
logits_ph, vf_preds_ph, phi1, phi2, asample = existing_inputs[:9]
# TODO: updates to account for s1, s2 and asample
existing_state_in = existing_inputs[9:-1]
existing_seq_lens = existing_inputs[-1]
else:
obs_ph = tf.placeholder(tf.float32,
name="obs",
shape=(None, ) + observation_space.shape)
adv_ph = tf.placeholder(tf.float32,
name="advantages",
shape=(None, ))
act_ph = ModelCatalog.get_action_placeholder(action_space)
logits_ph = tf.placeholder(tf.float32,
name="logits",
shape=(None, logit_dim))
vf_preds_ph = tf.placeholder(tf.float32,
name="vf_preds",
shape=(None, ))
value_targets_ph = tf.placeholder(tf.float32,
name="value_targets",
shape=(None, ))
phi1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_space.shape,
name="phi1")
phi2 = tf.placeholder(tf.float32,
shape=(None, ) + observation_space.shape,
name="phi2")
asample = tf.placeholder(tf.float32,
shape=(None, numaction),
name="asample")
existing_state_in = None
existing_seq_lens = None
self.observations = obs_ph
if self.unsup:
with tf.variable_scope("predictor"):
if 'state' in unsupType:
self.local_ap_network = predictor = StatePredictor(
phi1, phi2, asample, observation_space, numaction,
designHead, unsupType)
else:
self.local_ap_network = predictor = StateActionPredictor(
phi1, phi2, asample, observation_space, numaction,
designHead)
self.model = pi = ModelCatalog.get_model(obs_ph,
logit_dim,
self.config["model"],
state_in=existing_state_in,
seq_lens=existing_seq_lens)
# KL Coefficient
self.kl_coeff = tf.get_variable(initializer=tf.constant_initializer(
self.kl_coeff_val),
name="kl_coeff",
shape=(),
trainable=False,
dtype=tf.float32)
self.logits = self.model.outputs
curr_action_dist = dist_cls(self.logits)
self.sampler = curr_action_dist.sample()
if self.config["use_gae"]:
if self.config["vf_share_layers"]:
self.value_function = tf.reshape(
linear(self.model.last_layer, 1, "value",
normc_initializer(1.0)), [-1])
else:
vf_config = self.config["model"].copy()
# Do not split the last layer of the value function into
# mean parameters and standard deviation parameters and
# do not make the standard deviations free variables.
vf_config["free_log_std"] = False
vf_config["use_lstm"] = False
with tf.variable_scope("value_function"):
self.value_function = ModelCatalog.get_model(
obs_ph, 1, vf_config).outputs
self.value_function = tf.reshape(self.value_function, [-1])
else:
self.value_function = tf.zeros(shape=tf.shape(obs_ph)[:1])
self.loss_obj = PPOLoss(action_space,
value_targets_ph,
adv_ph,
act_ph,
logits_ph,
vf_preds_ph,
curr_action_dist,
self.value_function,
self.kl_coeff,
unsupType,
predictor,
entropy_coeff=self.config["entropy_coeff"],
clip_param=self.config["clip_param"],
vf_clip_param=self.config["vf_clip_param"],
vf_loss_coeff=self.config["vf_loss_coeff"],
use_gae=self.config["use_gae"])
LearningRateSchedule.__init__(self, self.config["lr"],
self.config["lr_schedule"])
self.loss_in = [
("obs", obs_ph),
("value_targets", value_targets_ph),
("advantages", adv_ph),
("actions", act_ph),
("logits", logits_ph),
("vf_preds", vf_preds_ph),
("s1", phi1),
("s2", phi2),
("asample", asample),
]
self.extra_inputs = ["s1", "s2", "asample"]
# TODO: testing to see if this lets me pass inputs to ICM
# self.variables = ray.experimental.TensorFlowVariables(self.loss_in, self.sess)
TFPolicyGraph.__init__(self,
observation_space,
action_space,
self.sess,
obs_input=obs_ph,
action_sampler=self.sampler,
loss=self.loss_obj.loss,
loss_inputs=self.loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=config["model"]["max_seq_len"])
self.sess.run(tf.global_variables_initializer())
self.explained_variance = explained_variance(value_targets_ph,
self.value_function)
self.stats_fetches = {
"cur_lr": tf.cast(self.cur_lr, tf.float64),
"total_loss": self.loss_obj.loss,
"policy_loss": self.loss_obj.mean_policy_loss,
"vf_loss": self.loss_obj.mean_vf_loss,
"vf_explained_var": self.explained_variance,
"kl": self.loss_obj.mean_kl,
"entropy": self.loss_obj.mean_entropy
}
def _build_layers_v2(self, input_dict, num_outputs, options):
def spy(sequences, state_in, state_out, seq_lens):
if len(sequences) == 1:
return 0 # don't capture inference inputs
# TF runs this function in an isolated context, so we have to use
# redis to communicate back to our suite
ray.experimental.internal_kv._internal_kv_put(
"rnn_spy_in_{}".format(RNNSpyModel.capture_index),
pickle.dumps({
"sequences": sequences,
"state_in": state_in,
"state_out": state_out,
"seq_lens": seq_lens
}),
overwrite=True)
RNNSpyModel.capture_index += 1
return 0
features = input_dict["obs"]
cell_size = 3
last_layer = add_time_dimension(features, self.seq_lens)
# Setup the LSTM cell
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(
tf.float32, [None, lstm.state_size.c], name="c")
h_in = tf.placeholder(
tf.float32, [None, lstm.state_size.h], name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(
lstm,
last_layer,
initial_state=state_in,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.state_out = list(lstm_state)
spy_fn = tf.py_func(
spy, [
last_layer,
self.state_in,
self.state_out,
self.seq_lens,
],
tf.int64,
stateful=True)
# Compute outputs
with tf.control_dependencies([spy_fn]):
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def _build_layers(self, inputs, num_outputs, options):
cell_size = options.get("lstm_cell_size", 256)
use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
distutils.version.LooseVersion("1.0.0"))
last_layer = add_time_dimension(inputs, self.seq_lens)
# Setup the LSTM cell
if use_tf100_api:
lstm1 = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
lstm2 = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
else:
lstm1 = rnn.rnn_cell.BasicLSTMCell(cell_size, state_is_tuple=True)
lstm2 = rnn.rnn_cell.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm1.state_size.c, np.float32),
np.zeros(lstm1.state_size.h, np.float32),
np.zeros(lstm2.state_size.c, np.float32),
np.zeros(lstm2.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c1_in, h1_in, c2_in, h2_in = self.state_in
else:
c1_in = tf.placeholder(tf.float32, [None, lstm1.state_size.c],
name="c1")
h1_in = tf.placeholder(tf.float32, [None, lstm1.state_size.h],
name="h1")
c2_in = tf.placeholder(tf.float32, [None, lstm2.state_size.c],
name="c2")
h2_in = tf.placeholder(tf.float32, [None, lstm2.state_size.h],
name="h2")
self.state_in = [c1_in, h1_in, c2_in, h2_in]
# Setup LSTM outputs
if use_tf100_api:
state1_in = rnn.LSTMStateTuple(c1_in, h1_in)
state2_in = rnn.LSTMStateTuple(c2_in, h2_in)
else:
state1_in = rnn.rnn_cell.LSTMStateTuple(c1_in, h1_in)
state2_in = rnn.rnn_cell.LSTMStateTuple(c2_in, h2_in)
lstm1_out, lstm1_state = tf.nn.dynamic_rnn(
lstm1,
last_layer,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
with tf.variable_scope("value_function"):
lstm2_out, lstm2_state = tf.nn.dynamic_rnn(
lstm2,
last_layer,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.value_function = tf.reshape(
linear(tf.reshape(lstm2_out, [-1, cell_size]), 1, "vf",
normc_initializer(0.01)), [-1])
self.state_out = list(lstm1_state) + list(lstm2_state)
# Compute outputs
last_layer = tf.reshape(lstm1_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def _build_layers_v2(self, input_dict, num_outputs, options):
def spy(sequences, state_in, state_out, seq_lens):
if len(sequences) == 1:
return 0 # don't capture inference inputs
# TF runs this function in an isolated context, so we have to use
# redis to communicate back to our suite
ray.experimental.internal_kv._internal_kv_put(
"rnn_spy_in_{}".format(RNNSpyModel.capture_index),
pickle.dumps({
"sequences": sequences,
"state_in": state_in,
"state_out": state_out,
"seq_lens": seq_lens
}),
overwrite=True)
RNNSpyModel.capture_index += 1
return 0
features = input_dict["obs"]
cell_size = 3
last_layer = add_time_dimension(features, self.seq_lens)
# Setup the LSTM cell
lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
self.state_init = [
np.zeros(lstm.state_size.c, np.float32),
np.zeros(lstm.state_size.h, np.float32)
]
# Setup LSTM inputs
if self.state_in:
c_in, h_in = self.state_in
else:
c_in = tf.placeholder(tf.float32, [None, lstm.state_size.c],
name="c")
h_in = tf.placeholder(tf.float32, [None, lstm.state_size.h],
name="h")
self.state_in = [c_in, h_in]
# Setup LSTM outputs
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_out, lstm_state = tf.nn.dynamic_rnn(lstm,
last_layer,
initial_state=state_in,
sequence_length=self.seq_lens,
time_major=False,
dtype=tf.float32)
self.state_out = list(lstm_state)
spy_fn = tf.py_func(spy, [
last_layer,
self.state_in,
self.state_out,
self.seq_lens,
],
tf.int64,
stateful=True)
# Compute outputs
with tf.control_dependencies([spy_fn]):
last_layer = tf.reshape(lstm_out, [-1, cell_size])
logits = linear(last_layer, num_outputs, "action",
normc_initializer(0.01))
return logits, last_layer
def __init__(self, observation_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
self.sess = tf.get_default_session()
# Setup the policy
self.observations = tf.placeholder(
tf.float32, [None] + list(observation_space.shape))
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_model(
self.observations, logit_dim, self.config["model"])
action_dist = dist_class(self.model.outputs)
self.vf = tf.reshape(
linear(self.model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
is_training = tf.placeholder_with_default(True, ())
# Setup the policy loss
if isinstance(action_space, gym.spaces.Box):
ac_size = action_space.shape[0]
actions = tf.placeholder(tf.float32, [None, ac_size], name="ac")
elif isinstance(action_space, gym.spaces.Discrete):
actions = tf.placeholder(tf.int64, [None], name="ac")
else:
raise UnsupportedSpaceException(
"Action space {} is not supported for A3C.".format(
action_space))
advantages = tf.placeholder(tf.float32, [None], name="advantages")
v_target = tf.placeholder(tf.float32, [None], name="v_target")
self.loss = A3CLoss(
action_dist, actions, advantages, v_target, self.vf,
self.config["vf_loss_coeff"], self.config["entropy_coeff"])
# Initialize TFPolicyGraph
loss_in = [
("obs", self.observations),
("actions", actions),
("advantages", advantages),
("value_targets", v_target),
]
for i, ph in enumerate(self.model.state_in):
loss_in.append(("state_in_{}".format(i), ph))
self.state_in = self.model.state_in
self.state_out = self.model.state_out
TFPolicyGraph.__init__(
self, observation_space, action_space, self.sess,
obs_input=self.observations, action_sampler=action_dist.sample(),
loss=self.loss.total_loss, loss_inputs=loss_in,
is_training=is_training, state_inputs=self.state_in,
state_outputs=self.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])
if self.config.get("summarize"):
bs = tf.to_float(tf.shape(self.observations)[0])
tf.summary.scalar("model/policy_graph", self.loss.pi_loss / bs)
tf.summary.scalar("model/value_loss", self.loss.vf_loss / bs)
tf.summary.scalar("model/entropy", self.loss.entropy / bs)
tf.summary.scalar("model/grad_gnorm", tf.global_norm(self._grads))
tf.summary.scalar("model/var_gnorm", tf.global_norm(self.var_list))
self.summary_op = tf.summary.merge_all()
self.sess.run(tf.global_variables_initializer())
def __init__(self, observation_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
assert config["batch_mode"] == "truncate_episodes", \
"Must use `truncate_episodes` batch mode with V-trace."
self.config = config
self.sess = tf.get_default_session()
# Setup the policy
self.observations = tf.placeholder(tf.float32, [None] +
list(observation_space.shape))
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_model(self.observations, logit_dim,
self.config["model"])
action_dist = dist_class(self.model.outputs)
values = tf.reshape(
linear(self.model.last_layer, 1, "value", normc_initializer(1.0)),
[-1])
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
# Setup the policy loss
if isinstance(action_space, gym.spaces.Box):
ac_size = action_space.shape[0]
actions = tf.placeholder(tf.float32, [None, ac_size], name="ac")
elif isinstance(action_space, gym.spaces.Discrete):
ac_size = action_space.n
actions = tf.placeholder(tf.int64, [None], name="ac")
else:
raise UnsupportedSpaceException(
"Action space {} is not supported for IMPALA.".format(
action_space))
dones = tf.placeholder(tf.bool, [None], name="dones")
rewards = tf.placeholder(tf.float32, [None], name="rewards")
behaviour_logits = tf.placeholder(tf.float32, [None, ac_size],
name="behaviour_logits")
def to_batches(tensor):
if self.config["model"]["use_lstm"]:
B = tf.shape(self.model.seq_lens)[0]
T = tf.shape(tensor)[0] // B
else:
# Important: chop the tensor into batches at known episode cut
# boundaries. TODO(ekl) this is kind of a hack
T = (self.config["sample_batch_size"] //
self.config["num_envs_per_worker"])
B = tf.shape(tensor)[0] // T
rs = tf.reshape(tensor,
tf.concat([[B, T], tf.shape(tensor)[1:]], axis=0))
# swap B and T axes
return tf.transpose(
rs,
[1, 0] + list(range(2, 1 + int(tf.shape(tensor).shape[0]))))
if self.config["clip_rewards"]:
clipped_rewards = tf.clip_by_value(rewards, -1, 1)
else:
clipped_rewards = rewards
# Inputs are reshaped from [B * T] => [T - 1, B] for V-trace calc.
self.loss = VTraceLoss(
actions=to_batches(actions)[:-1],
actions_logp=to_batches(action_dist.logp(actions))[:-1],
actions_entropy=to_batches(action_dist.entropy())[:-1],
dones=to_batches(dones)[:-1],
behaviour_logits=to_batches(behaviour_logits)[:-1],
target_logits=to_batches(self.model.outputs)[:-1],
discount=config["gamma"],
rewards=to_batches(clipped_rewards)[:-1],
values=to_batches(values)[:-1],
bootstrap_value=to_batches(values)[-1],
vf_loss_coeff=self.config["vf_loss_coeff"],
entropy_coeff=self.config["entropy_coeff"],
clip_rho_threshold=self.config["vtrace_clip_rho_threshold"],
clip_pg_rho_threshold=self.config["vtrace_clip_pg_rho_threshold"])
# Initialize TFPolicyGraph
loss_in = [
("actions", actions),
("dones", dones),
("behaviour_logits", behaviour_logits),
("rewards", rewards),
("obs", self.observations),
]
TFPolicyGraph.__init__(self,
observation_space,
action_space,
self.sess,
obs_input=self.observations,
action_sampler=action_dist.sample(),
loss=self.loss.total_loss,
loss_inputs=loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])
self.sess.run(tf.global_variables_initializer())
