def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
policy_params = {
str(i): v
for i, v in enumerate(
itertools.chain.from_iterable(self.policy.parameters()))
}
data_logger.log_values_dict("policy/param", policy_params, step)
data_logger.create_histogram('metric/batch_errors',
self._metric_batch_error, step)
data_logger.create_histogram('metric/batch_value_dist',
self._metric_batch_value_dist, step)
if full_log:
dist = self._display_dist
z_atoms = self.critic.z_atoms
z_delta = self.critic.z_delta
data_logger.add_histogram('dist/dist_value',
min=z_atoms[0],
max=z_atoms[-1],
num=self.num_atoms,
sum=dist.sum(),
sum_squares=dist.pow(2).sum(),
bucket_limits=z_atoms + z_delta,
bucket_counts=dist,
global_step=step)
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
for metric_name, metric_value in self._metrics.items():
data_logger.log_value(metric_name, metric_value, step)
policy_params = {
str(i): v
for i, v in enumerate(
itertools.chain.from_iterable(self.policy.parameters()))
}
data_logger.log_values_dict("policy/param", policy_params, step)
if full_log:
for idx, layer in enumerate(self.actor.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"actor/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"actor/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.critic.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic/layer_bias_{idx}",
layer.bias, step)
def log_metrics(self, data_logger: DataLogger, step, full_log=False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
policy_params = {
str(i): v
for i, v in enumerate(
itertools.chain.from_iterable(self.policy.parameters()))
}
data_logger.log_values_dict("policy/param", policy_params, step)
data_logger.create_histogram('metric/batch_errors',
self._metric_batch_error.sum(-1), step)
data_logger.create_histogram('metric/batch_value_dist',
self._batch_value_dist_metric, step)
# This method, `log_metrics`, isn't executed on every iteration but just in case we delay plotting weights.
# It simply might be quite costly. Thread wisely.
if full_log:
for idx, layer in enumerate(self.actor.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"actor/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"actor/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.critic.net.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic/layer_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic/layer_bias_{idx}",
layer.bias, step)
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
data_logger.log_value("loss/alpha", self.alpha, step)
if self.simple_policy:
policy_params = {
str(i): v
for i, v in enumerate(
itertools.chain.from_iterable(self.policy.parameters()))
}
data_logger.log_values_dict("policy/param", policy_params, step)
for name, value in self._metrics.items():
if isinstance(value, dict):
data_logger.log_values_dict(name, value, step)
else:
data_logger.log_value(name, value, step)
if full_log:
# TODO: Add Policy layers
for idx, layer in enumerate(self.actor.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"policy/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"policy/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.double_critic.critic_1.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic_1/layer_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic_1/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.double_critic.critic_2.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic_2/layer_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic_2/layer_bias_{idx}",
layer.bias, step)
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
for agent_name, agent in self.agents.items():
data_logger.log_values_dict(f"{agent_name}/loss", agent.loss, step)