def __init__(self,
state_normalization_parameters: Dict[str,
NormalizationParameters],
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
skip_normalization: Optional[bool] = False) -> None:
print(state_normalization_parameters)
print(parameters)
self._state_normalization_parameters = state_normalization_parameters
MLTrainer.__init__(self, "rl_trainer", parameters.training)
self.target_network = TargetNetwork(self,
parameters.rl.target_update_rate)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.training_iteration = 0
self._buffers = None
self.minibatch_size = parameters.training.minibatch_size
self.skip_normalization = skip_normalization
self._prepare_state_normalization()
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
assert parameters.training.layers[0] >= 0,\
"Set layers[0] to a the number of features"
self.num_features = parameters.training.layers[0]
MLTrainer.__init__(self, RL_TRAINER_MODEL_ID, parameters.training)
self.target_network = TargetNetwork(
self, parameters.rl.target_update_rate
)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.rl_temperature = parameters.rl.temperature
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
self.parameters = parameters
self.loss_blob: Optional[str] = None
workspace.FeedBlob('states', np.array([0], dtype=np.float32))
workspace.FeedBlob('actions', np.array([0], dtype=np.float32))
workspace.FeedBlob('rewards', np.array([0], dtype=np.float32))
workspace.FeedBlob('next_states', np.array([0], dtype=np.float32))
workspace.FeedBlob('not_terminals', np.array([0], dtype=np.float32))
workspace.FeedBlob('next_actions', np.array([0], dtype=np.float32))
workspace.FeedBlob(
'possible_next_actions', np.array([0], dtype=np.float32)
)
workspace.FeedBlob(
'possible_next_actions_lengths', np.array([0], dtype=np.float32)
)
self.rl_train_model: Optional[ModelHelper] = None
self.reward_train_model: Optional[ModelHelper] = None
self.q_score_model: Optional[ModelHelper] = None
self._create_reward_train_net()
self._create_rl_train_net()
self._create_q_score_net()
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
assert parameters.training.layers[0] >= 0,\
"Set layers[0] to a the number of features"
self.num_features = parameters.training.layers[0]
MLTrainer.__init__(self, "rl_trainer", parameters.training)
self.target_network = TargetNetwork(self,
parameters.rl.target_update_rate)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
def __init__(
self,
fc_parameters: DiscreteActionModelParameters,
cnn_parameters: CNNModelParameters,
img_height: int,
img_width: int,
) -> None:
MLConvTrainer.__init__(self, "ml_conv_trainer", fc_parameters.training,
cnn_parameters, img_height, img_width)
self.target_network = TargetNetwork(
self, fc_parameters.rl.target_update_rate)
self.reward_burnin = fc_parameters.rl.reward_burnin
self.maxq_learning = fc_parameters.rl.maxq_learning
self.rl_discount_rate = fc_parameters.rl.gamma
self.training_iteration = 0
self._buffers = None
self.minibatch_size = fc_parameters.training.minibatch_size
self.skip_normalization = True
class RLTrainer(MLTrainer):
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
assert parameters.training.layers[0] >= 0,\
"Set layers[0] to a the number of features"
self.num_features = parameters.training.layers[0]
MLTrainer.__init__(self, "rl_trainer", parameters.training)
self.target_network = TargetNetwork(self,
parameters.rl.target_update_rate)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
@property
def sarsa(self) -> bool:
"""
Returns whether or not this trainer generates target values using SARSA.
"""
return not self.maxq_learning
def stream_tdp(self,
tdp: TrainingDataPage,
evaluator: Optional[Evaluator] = None) -> None:
"""
Loads a large batch of transitions from a page of training data. This
batch will be further broken down into minibatches for training.
:param tdp: TrainingDataPage object that supplies transitions.
:param evaluator: Evaluator object to record TD and compute MC losses.
"""
raise NotImplementedError()
def get_max_q_values(self, next_states: np.ndarray,
possible_next_actions) -> np.ndarray:
"""
Takes in an array of next_states and outputs an array of the same shape
whose ith entry = max_{pna} Q(state_i, pna). Uses target network for
Q(state_i, pna) approximation.
:param next_states: Numpy array with shape (batch_size, state_dim). Each
row contains a representation of a state.
:param possible_next_actions: See subclass' `get_max_q_values` documentation.
"""
raise NotImplementedError()
def get_sarsa_values(self, next_states: np.ndarray,
next_actions: np.ndarray) -> np.ndarray:
"""
Takes in a set of next_states and corresponding next_actions. For each
(next_state_i, next_action_i) pair, calculates Q(next_state, next_action).
Returns these q values in a Numpy array of shape (batch_size, 1).
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next_state.
:param next_actions: See subclass' `get_sarsa_values` documentation.
"""
raise NotImplementedError()
def update_model(self, states: np.ndarray, actions: np.ndarray,
q_vals_target: np.ndarray) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets.
Updates Q Network's weights according to loss and optimizer.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row is a representation of the ith transition's action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
raise NotImplementedError()
def stream(self, states, actions, rewards, next_states, next_actions,
not_terminals, possible_next_actions, reward_timelines,
evaluator):
"""
Load large batch as training set. This batch will be broken down into
minibatches. Assumes that states, next_states, and actions (in the
parametric action case) need no further normalization.
"""
assert rewards.ndim == 2
assert not_terminals.ndim == 2
page_size = states.shape[0]
assert page_size == self.minibatch_size
self.train(
states,
actions,
rewards,
next_states,
next_actions,
not_terminals,
possible_next_actions,
)
if evaluator is not None:
evaluator.report(
reward_timelines,
self.get_q_values(states, actions),
workspace.FetchBlob(self.loss_blob),
)
def train(
self,
states: np.ndarray,
actions: np.ndarray,
rewards: np.ndarray,
next_states: np.ndarray,
next_actions: Optional[np.ndarray],
not_terminals: np.ndarray,
possible_next_actions,
) -> None:
"""
Takes in a batch of transitions. For transition i, calculates target qval:
next_q_values_i = {
max_{pna_i} Q(next_state_i, pna_i), self.maxq_learning
Q(next_state_i, next_action_i), self.sarsa
}
q_val_target_i = {
r_i + gamma * next_q_values_i, not_terminals_i
r_i, !not_terminals_i
}
Trains Q Network on the q_val_targets as labels.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: See subclass' `train` documentation.
:param rewards: Numpy array with shape (batch_size, 1). The ith entry is
the reward experienced at the ith transition.
:param not_terminals: Numpy array with shape (batch_size, 1). The ith entry
is equal to 1 iff the ith transition's state is not terminal.
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next state.
:param next_actions: See subclass' `train` documentation.
:param possible_next_actions: See subclass' `train` documentation.
"""
batch_size = self.minibatch_size
assert rewards.shape == (
batch_size, 1
), "Invalid reward shape: " + \
str(rewards.shape) + " != " + str(self.minibatch_size)
assert rewards.dtype == np.float32
assert not_terminals.shape == (
batch_size, 1), 'terminals invalid ' + str(not_terminals.shape)
q_vals_target = np.copy(rewards)
if self.training_iteration >= self.reward_burnin:
if self.training_iteration == self.reward_burnin:
logger.info(
"Minibatch number == reward_burnin. Starting RL updates.")
if self.maxq_learning:
next_q_values = self.get_max_q_values(next_states,
possible_next_actions)
else:
next_q_values = self.get_sarsa_values(next_states,
next_actions)
q_vals_target += not_terminals * self.rl_discount_rate * next_q_values
self.update_model(states, actions, q_vals_target)
if self.training_iteration >= self.reward_burnin:
self.target_network.enable_slow_updates()
self.target_network.target_update()
self.training_iteration += 1
class RLTrainer(MLTrainer):
def __init__(self,
state_normalization_parameters: Dict[str,
NormalizationParameters],
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
skip_normalization: Optional[bool] = False) -> None:
print(state_normalization_parameters)
print(parameters)
self._state_normalization_parameters = state_normalization_parameters
MLTrainer.__init__(self, "rl_trainer", parameters.training)
self.target_network = TargetNetwork(self,
parameters.rl.target_update_rate)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.training_iteration = 0
self._buffers = None
self.minibatch_size = parameters.training.minibatch_size
self.skip_normalization = skip_normalization
self._prepare_state_normalization()
def _normalize_states(self, states: np.ndarray) -> np.ndarray:
"""
Normalizes input states and replaces NaNs with 0. Returns a matrix of
the same shape. Make sure to have set up the underlying normalization net
with `_prepare_state_normalization`.
:param states: Numpy array with shape (batch_size, state_dim) containing
raw state inputs
"""
if self.skip_normalization:
return states
return normalize_dense_matrix(states, self._state_features,
self._state_normalization_parameters,
self.state_norm_blobs,
self.state_norm_net,
self.state_norm_blobname_template,
self.num_state_features)
def _prepare_state_normalization(self):
"""
Sets up operators for action normalization net.
"""
if self.skip_normalization:
return
self._state_features = list(
self._state_normalization_parameters.keys())
self.state_norm_net = core.Net("state_norm_net")
self.state_norm_blobname_template = '{}_input_state'
self.state_norm_blobs = prepare_normalization(
self.state_norm_net, self._state_normalization_parameters,
self._state_features, self.state_norm_blobname_template, True)
def get_state_features(self) -> List[str]:
return self._state_features
@property
def num_state_features(self) -> int:
"""
Returns the number of features in each preprocessed state.
"""
raise NotImplementedError()
@property
def sarsa(self) -> bool:
"""
Returns whether or not this trainer generates target values using SARSA.
"""
return not self.maxq_learning
def predictor(self) -> RLPredictor:
"""
Builds a Predictor using the networks undrlying this Trainer.
"""
raise NotImplementedError()
def stream_tdp(self,
tdp: TrainingDataPage,
evaluator: Optional[Evaluator] = None) -> None:
"""
Loads a large batch of transitions from a page of training data. This
batch will be further broken down into minibatches for training.
:param tdp: TrainingDataPage object that supplies transitions.
:param evaluator: Evaluator object to record TD and compute MC losses.
"""
raise NotImplementedError()
def get_max_q_values(self, next_states: np.ndarray,
possible_next_actions) -> np.ndarray:
"""
Takes in an array of next_states and outputs an array of the same shape
whose ith entry = max_{pna} Q(state_i, pna). Uses target network for
Q(state_i, pna) approximation.
:param next_states: Numpy array with shape (batch_size, state_dim). Each
row contains a representation of a state.
:param possible_next_actions: See subclass' `get_max_q_values` documentation.
"""
raise NotImplementedError()
def get_sarsa_values(self, next_states: np.ndarray,
next_actions: np.ndarray) -> np.ndarray:
"""
Takes in a set of next_states and corresponding next_actions. For each
(next_state_i, next_action_i) pair, calculates Q(next_state, next_action).
Returns these q values in a Numpy array of shape (batch_size, 1).
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next_state.
:param next_actions: See subclass' `get_sarsa_values` documentation.
"""
raise NotImplementedError()
def update_model(self, states: np.ndarray, actions: np.ndarray,
q_vals_target: np.ndarray) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets.
Updates Q Network's weights according to loss and optimizer.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row is a representation of the ith transition's action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
raise NotImplementedError()
def _validate_train_inputs(
self,
states: np.ndarray,
actions: np.ndarray,
rewards: np.ndarray,
next_states: np.ndarray,
next_actions: Optional[np.ndarray],
not_terminals: np.ndarray,
possible_next_actions: np.ndarray,
):
raise NotImplementedError()
def stream(self, states, actions, rewards, next_states, next_actions,
not_terminals, possible_next_actions, reward_timelines,
evaluator):
"""
Load large batch as training set. This batch will be broken down into
minibatches. Assumes that states, next_states, and actions (in the
parametric action case) need no further normalization.
"""
if rewards.ndim == 1:
rewards = rewards.reshape(-1, 1)
if not_terminals.ndim == 1:
not_terminals = not_terminals.reshape(-1, 1)
use_next_actions = next_actions is not None and self.sarsa
use_pna = possible_next_actions is not None and self.maxq_learning
use_rt = reward_timelines is not None
num_buffers = 8
if self._buffers is not None and self._buffers[0].shape[0] > 0:
actions = np.concatenate([self._buffers[0], actions])
states = np.concatenate([self._buffers[1], states])
rewards = np.concatenate([self._buffers[2], rewards])
next_states = np.concatenate([self._buffers[3], next_states])
if use_next_actions:
next_actions = np.concatenate([self._buffers[4], next_actions])
not_terminals = np.concatenate([self._buffers[5], not_terminals])
if use_pna:
possible_next_actions = np.concatenate(
[self._buffers[6], possible_next_actions])
if use_rt:
reward_timelines = np.concatenate(
[self._buffers[7], reward_timelines])
self._buffers = None
page_size = states.shape[0]
for batch_start in range(0, page_size, self.minibatch_size):
batch_end = batch_start + self.minibatch_size
if page_size < batch_end:
self._buffers = [[] for _ in range(num_buffers)]
self._buffers[0] = actions[batch_start:]
self._buffers[1] = states[batch_start:]
self._buffers[2] = rewards[batch_start:]
self._buffers[3] = next_states[batch_start:]
if use_next_actions:
self._buffers[4] = next_actions[batch_start:]
self._buffers[5] = not_terminals[batch_start:]
if use_pna:
self._buffers[6] = possible_next_actions[batch_start:]
if use_rt:
self._buffers[7] = reward_timelines[batch_start:]
else:
na_batch = (next_actions[batch_start:batch_end]
if use_next_actions else None)
pna_batch = (possible_next_actions[batch_start:batch_end]
if use_pna else None)
rt_batch = (reward_timelines[batch_start:batch_end]
if use_rt else None)
states_batch = states[batch_start:batch_end]
actions_batch = actions[batch_start:batch_end]
self.train(states_batch, actions_batch,
rewards[batch_start:batch_end],
next_states[batch_start:batch_end], na_batch,
not_terminals[batch_start:batch_end], pna_batch)
if evaluator is not None:
evaluator.report(
rt_batch, self.get_q_values(states_batch,
actions_batch),
workspace.FetchBlob(self.loss_blob))
def train(self, states: np.ndarray, actions: np.ndarray,
rewards: np.ndarray, next_states: np.ndarray,
next_actions: Optional[np.ndarray], not_terminals: np.ndarray,
possible_next_actions: Optional[List]) -> None:
"""
Takes in a batch of transitions. For transition i, calculates target qval:
next_q_values_i = {
max_{pna_i} Q(next_state_i, pna_i), self.maxq_learning
Q(next_state_i, next_action_i), self.sarsa
}
q_val_target_i = {
r_i + gamma * next_q_values_i, not_terminals_i
r_i, !not_terminals_i
}
Trains Q Network on the q_val_targets as labels.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: See subclass' `train` documentation.
:param rewards: Numpy array with shape (batch_size, 1). The ith entry is
the reward experienced at the ith transition.
:param not_terminals: Numpy array with shape (batch_size, 1). The ith entry
is equal to 1 iff the ith transition's state is not terminal.
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next state.
:param next_actions: See subclass' `train` documentation.
:param possible_next_actions: See subclass' `train` documentation.
"""
self._validate_train_inputs(states, actions, rewards, next_states,
next_actions, not_terminals,
possible_next_actions)
batch_size = self.minibatch_size
assert rewards.shape == (batch_size, 1)
assert rewards.dtype == np.float32
assert not_terminals.shape == (batch_size, 1)
q_vals_target = np.copy(rewards)
if self.training_iteration >= self.reward_burnin:
if self.training_iteration == self.reward_burnin:
logger.info(
"Minibatch number == reward_burnin. Starting RL updates.")
if self.maxq_learning:
next_q_values = self.get_max_q_values(next_states,
possible_next_actions)
else:
next_q_values = self.get_sarsa_values(next_states,
next_actions)
q_vals_target += not_terminals * self.rl_discount_rate * next_q_values
self.update_model(states, actions, q_vals_target)
if self.training_iteration >= self.reward_burnin:
self.target_network.enable_slow_updates()
self.target_network.target_update()
self.training_iteration += 1
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
RLTrainer.num_trainers += 1
self.model_id = RL_TRAINER_PREFIX + str(RLTrainer.num_trainers)
if parameters.training.cnn_parameters is not None:
self.conv_ml_trainer = ConvMLTrainer(
CONV_ML_TRAINER_PREFIX + str(RLTrainer.num_trainers),
parameters.training.cnn_parameters,
)
# The final layer of the conv net is the input to the fc net.
parameters.training.layers[
0] = self.conv_ml_trainer.get_output_size()
self.conv_target_network = ConvTargetNetwork(
CONV_TARGET_NETWORK_PREFIX + str(RLTrainer.num_trainers),
parameters.training.cnn_parameters,
parameters.rl.target_update_rate,
self.conv_ml_trainer,
)
else:
self.conv_ml_trainer = None
self.conv_target_network = None
assert (parameters.training.layers[0] >=
0), "Set layers[0] to a the number of features"
self.ml_trainer = MLTrainer(
ML_TRAINER_PREFIX + str(RLTrainer.num_trainers),
parameters.training)
self.target_network = TargetNetwork(
TARGET_NETWORK_PREFIX + str(RLTrainer.num_trainers),
parameters.training,
parameters.rl.target_update_rate,
self.ml_trainer,
)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.rl_temperature = parameters.rl.temperature
self.use_seq_num_diff_as_time_diff = parameters.rl.use_seq_num_diff_as_time_diff
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
self.parameters = parameters
self.loss_blob: Optional[str] = None
workspace.FeedBlob("states", np.array([0], dtype=np.float32))
workspace.FeedBlob("actions", np.array([0], dtype=np.float32))
workspace.FeedBlob("rewards", np.array([0], dtype=np.float32))
workspace.FeedBlob("next_states", np.array([0], dtype=np.float32))
workspace.FeedBlob("not_terminals", np.array([0], dtype=np.float32))
if self.maxq_learning:
workspace.FeedBlob("possible_next_actions",
np.array([0], dtype=np.float32))
workspace.FeedBlob("possible_next_actions_lengths",
np.array([0], dtype=np.float32))
else:
workspace.FeedBlob("next_actions", np.array([0], dtype=np.float32))
# Setting to 1 serves as a 1 unit time_diff if not set by user
workspace.FeedBlob("time_diff", np.array([1], dtype=np.float32))
self.rl_train_model: Optional[ModelHelper] = None
self.reward_train_model: Optional[ModelHelper] = None
self.q_score_model: Optional[ModelHelper] = None
self._create_reward_train_net()
self._create_rl_train_net()
self._create_q_score_net()
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
class RLTrainer:
num_trainers = 0
DEFAULT_TRAINING_NUM_WORKERS = 4
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
RLTrainer.num_trainers += 1
self.model_id = RL_TRAINER_PREFIX + str(RLTrainer.num_trainers)
if parameters.training.cnn_parameters is not None:
self.conv_ml_trainer = ConvMLTrainer(
CONV_ML_TRAINER_PREFIX + str(RLTrainer.num_trainers),
parameters.training.cnn_parameters,
)
# The final layer of the conv net is the input to the fc net.
parameters.training.layers[
0] = self.conv_ml_trainer.get_output_size()
self.conv_target_network = ConvTargetNetwork(
CONV_TARGET_NETWORK_PREFIX + str(RLTrainer.num_trainers),
parameters.training.cnn_parameters,
parameters.rl.target_update_rate,
self.conv_ml_trainer,
)
else:
self.conv_ml_trainer = None
self.conv_target_network = None
assert (parameters.training.layers[0] >=
0), "Set layers[0] to a the number of features"
self.ml_trainer = MLTrainer(
ML_TRAINER_PREFIX + str(RLTrainer.num_trainers),
parameters.training)
self.target_network = TargetNetwork(
TARGET_NETWORK_PREFIX + str(RLTrainer.num_trainers),
parameters.training,
parameters.rl.target_update_rate,
self.ml_trainer,
)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.rl_temperature = parameters.rl.temperature
self.use_seq_num_diff_as_time_diff = parameters.rl.use_seq_num_diff_as_time_diff
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
self.parameters = parameters
self.loss_blob: Optional[str] = None
workspace.FeedBlob("states", np.array([0], dtype=np.float32))
workspace.FeedBlob("actions", np.array([0], dtype=np.float32))
workspace.FeedBlob("rewards", np.array([0], dtype=np.float32))
workspace.FeedBlob("next_states", np.array([0], dtype=np.float32))
workspace.FeedBlob("not_terminals", np.array([0], dtype=np.float32))
if self.maxq_learning:
workspace.FeedBlob("possible_next_actions",
np.array([0], dtype=np.float32))
workspace.FeedBlob("possible_next_actions_lengths",
np.array([0], dtype=np.float32))
else:
workspace.FeedBlob("next_actions", np.array([0], dtype=np.float32))
# Setting to 1 serves as a 1 unit time_diff if not set by user
workspace.FeedBlob("time_diff", np.array([1], dtype=np.float32))
self.rl_train_model: Optional[ModelHelper] = None
self.reward_train_model: Optional[ModelHelper] = None
self.q_score_model: Optional[ModelHelper] = None
self._create_reward_train_net()
self._create_rl_train_net()
self._create_q_score_net()
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
def get_possible_next_actions(self):
raise NotImplementedError()
def get_max_q_values(self, next_states: str, possible_next_actions,
use_target_network: bool) -> str:
"""
Takes in an array of next_states and outputs an array of the same shape
whose ith entry = max_{pna} Q(state_i, pna). Uses target network for
Q(state_i, pna) approximation.
:param next_states: Numpy array with shape (batch_size, state_dim). Each
row contains a representation of a state.
:param possible_next_actions: See subclass' `get_max_q_values` documentation.
"""
raise NotImplementedError()
def get_q_values(self, states: str, actions: str,
use_target_network: bool) -> str:
"""
Takes in a set of next_states and corresponding next_actions. For each
(next_state_i, next_action_i) pair, calculates Q(next_state, next_action).
Returns these q values in a Numpy array of shape (batch_size, 1).
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next_state.
:param next_actions: See subclass' `get_sarsa_values` documentation.
"""
raise NotImplementedError()
def update_model(self, states: str, actions: str,
q_vals_target: str) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets.
Updates Q Network's weights according to loss and optimizer.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row is a representation of the ith transition's action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
raise NotImplementedError()
def _create_reward_train_net(self) -> None:
raise NotImplementedError()
def _create_rl_train_net(self) -> None:
raise NotImplementedError()
def _create_q_score_net(self) -> None:
self.q_score_model = ModelHelper(name="q_score_" + self.model_id)
C2.set_model(self.q_score_model)
self.q_score_output = self.get_q_values("states", "actions", True)
workspace.RunNetOnce(self.q_score_model.param_init_net)
self.q_score_model.net.Proto().num_workers = (
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS)
self.q_score_model.net.Proto().type = "async_scheduling"
workspace.CreateNet(self.q_score_model.net)
C2.set_model(None)
def train_numpy(self, tdp: TrainingDataPage,
evaluator: Optional[Evaluator]):
workspace.FeedBlob("states", tdp.states)
workspace.FeedBlob("actions", tdp.actions)
workspace.FeedBlob("rewards", tdp.rewards)
workspace.FeedBlob("next_states", tdp.next_states)
workspace.FeedBlob("not_terminals", tdp.not_terminals)
workspace.FeedBlob("time_diff", np.array([1], dtype=np.float32))
if self.maxq_learning:
if isinstance(tdp.possible_next_actions, StackedArray):
workspace.FeedBlob("possible_next_actions",
tdp.possible_next_actions.values)
workspace.FeedBlob("possible_next_actions_lengths",
tdp.possible_next_actions.lengths)
else:
workspace.FeedBlob("possible_next_actions",
tdp.possible_next_actions)
else:
workspace.FeedBlob("next_actions", tdp.next_actions)
self.train()
if evaluator is not None:
self.evaluate(evaluator, tdp.actions, tdp.propensities,
tdp.episode_values)
def train(self) -> None:
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
if self.training_iteration >= self.reward_burnin:
if self.training_iteration == self.reward_burnin:
logger.info(
"Minibatch number == reward_burnin. Starting RL updates.")
self.target_network.enable_slow_updates()
if self.conv_target_network:
self.conv_target_network.enable_slow_updates()
workspace.RunNet(self.rl_train_model.net)
else:
workspace.RunNet(self.reward_train_model.net)
workspace.RunNet(self.target_network._update_model.net)
if self.conv_target_network:
workspace.RunNet(self.conv_target_network._update_model.net)
self.training_iteration += 1
workspace.RunNet(self.q_score_model.net)
def evaluate(
self,
evaluator: Optional[Evaluator],
logged_actions: Optional[np.ndarray],
logged_propensities: Optional[np.ndarray],
logged_values: Optional[np.ndarray],
):
raise NotImplementedError()
def build_predictor(self, model, input_blob, output_blob) -> List[str]:
retval: List[str] = []
if self.conv_ml_trainer is not None:
conv_output = model.net.NextBlob("conv_output")
retval = self.conv_ml_trainer.build_predictor(
model, input_blob, conv_output)
conv_output_flat = model.net.NextBlob("conv_output_flat")
model.net.Flatten([conv_output], [conv_output_flat])
input_blob = conv_output_flat
retval += self.ml_trainer.build_predictor(model, input_blob,
output_blob)
return retval
class RLTrainer(MLTrainer):
def __init__(
self,
parameters: Union[DiscreteActionModelParameters,
ContinuousActionModelParameters],
) -> None:
logger.info(str(parameters))
assert parameters.training.layers[0] >= 0,\
"Set layers[0] to a the number of features"
self.num_features = parameters.training.layers[0]
MLTrainer.__init__(self, RL_TRAINER_MODEL_ID, parameters.training)
self.target_network = TargetNetwork(
self, parameters.rl.target_update_rate
)
self.reward_burnin = parameters.rl.reward_burnin
self.maxq_learning = parameters.rl.maxq_learning
self.rl_discount_rate = parameters.rl.gamma
self.rl_temperature = parameters.rl.temperature
self.training_iteration = 0
self.minibatch_size = parameters.training.minibatch_size
self.parameters = parameters
self.loss_blob: Optional[str] = None
workspace.FeedBlob('states', np.array([0], dtype=np.float32))
workspace.FeedBlob('actions', np.array([0], dtype=np.float32))
workspace.FeedBlob('rewards', np.array([0], dtype=np.float32))
workspace.FeedBlob('next_states', np.array([0], dtype=np.float32))
workspace.FeedBlob('not_terminals', np.array([0], dtype=np.float32))
workspace.FeedBlob('next_actions', np.array([0], dtype=np.float32))
workspace.FeedBlob(
'possible_next_actions', np.array([0], dtype=np.float32)
)
workspace.FeedBlob(
'possible_next_actions_lengths', np.array([0], dtype=np.float32)
)
self.rl_train_model: Optional[ModelHelper] = None
self.reward_train_model: Optional[ModelHelper] = None
self.q_score_model: Optional[ModelHelper] = None
self._create_reward_train_net()
self._create_rl_train_net()
self._create_q_score_net()
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
def get_possible_next_actions(self):
raise NotImplementedError()
def get_max_q_values(
self,
next_states: str,
possible_next_actions,
use_target_network: bool,
) -> str:
"""
Takes in an array of next_states and outputs an array of the same shape
whose ith entry = max_{pna} Q(state_i, pna). Uses target network for
Q(state_i, pna) approximation.
:param next_states: Numpy array with shape (batch_size, state_dim). Each
row contains a representation of a state.
:param possible_next_actions: See subclass' `get_max_q_values` documentation.
"""
raise NotImplementedError()
def get_q_values(
self,
states: str,
actions: str,
use_target_network: bool,
) -> str:
"""
Takes in a set of next_states and corresponding next_actions. For each
(next_state_i, next_action_i) pair, calculates Q(next_state, next_action).
Returns these q values in a Numpy array of shape (batch_size, 1).
:param next_states: Numpy array with shape (batch_size, state_dim). The
ith row is a representation of the ith transition's next_state.
:param next_actions: See subclass' `get_sarsa_values` documentation.
"""
raise NotImplementedError()
def update_model(
self,
states: str,
actions: str,
q_vals_target: str,
) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets.
Updates Q Network's weights according to loss and optimizer.
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row is a representation of the ith transition's action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
raise NotImplementedError()
def _create_reward_train_net(self) -> None:
raise NotImplementedError()
def _create_rl_train_net(self) -> None:
raise NotImplementedError()
def _create_q_score_net(self) -> None:
self.q_score_model = ModelHelper(name="q_score_" + self.model_id)
C2.set_model(self.q_score_model)
self.q_score_output = self.get_q_values('states', 'actions', True)
workspace.RunNetOnce(self.q_score_model.param_init_net)
workspace.CreateNet(self.q_score_model.net)
C2.set_model(None)
def train_numpy(
self,
tdp: TrainingDataPage,
evaluator: Optional[Evaluator],
):
workspace.FeedBlob('states', tdp.states)
workspace.FeedBlob('actions', tdp.actions)
workspace.FeedBlob('rewards', tdp.rewards)
workspace.FeedBlob('next_states', tdp.next_states)
workspace.FeedBlob('not_terminals', tdp.not_terminals)
if self.maxq_learning:
if isinstance(tdp.possible_next_actions, StackedArray):
workspace.FeedBlob(
'possible_next_actions', tdp.possible_next_actions.values
)
workspace.FeedBlob(
'possible_next_actions_lengths',
tdp.possible_next_actions.lengths
)
else:
workspace.FeedBlob(
'possible_next_actions', tdp.possible_next_actions
)
else:
workspace.FeedBlob('next_actions', tdp.next_actions)
self.train(tdp.reward_timelines, evaluator)
def train(
self,
reward_timelines: Optional[List[Dict[int, float]]],
evaluator: Optional[Evaluator],
) -> None:
assert self.rl_train_model is not None
assert self.reward_train_model is not None
assert self.q_score_model is not None
if self.training_iteration >= self.reward_burnin:
if self.training_iteration == self.reward_burnin:
logger.info(
"Minibatch number == reward_burnin. Starting RL updates."
)
self.target_network.enable_slow_updates()
workspace.RunNet(self.rl_train_model.net)
else:
workspace.RunNet(self.reward_train_model.net)
self.target_network.target_update()
self.training_iteration += 1
workspace.RunNet(self.q_score_model.net)
if evaluator is not None:
assert reward_timelines is not None
assert self.loss_blob is not None
evaluator.report(
reward_timelines,
workspace.FetchBlob(self.q_score_output),
workspace.FetchBlob(self.loss_blob),
)
