View source on GitHub |
Metric to compute the average return for multiple metrics.
Inherits From: TFMultiMetricStepMetric
, TFStepMetric
tf_agents.metrics.tf_metrics.AverageReturnMultiMetric(
reward_spec,
name='AverageReturnMultiMetric',
prefix='Metrics',
dtype=tf.float32,
batch_size=1,
buffer_size=10
)
Used in the notebooks
Used in the tutorials |
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Attributes | |
---|---|
metric_names
|
Methods
call
call(
trajectory
)
init_variables
init_variables()
Initializes this Metric's variables.
Should be called after variables are created in the first execution
of __call__()
. If using graph execution, the return value should be
run()
in a session before running the op returned by __call__()
.
(See example above.)
Returns | |
---|---|
If using graph execution, this returns an op to perform the initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None. |
reset
reset()
result
result()
Computes and returns a final value for the metric.
tf_summaries
tf_summaries(
train_step=None, step_metrics=()
)
Generates per-metric summaries against train_step
and step_metrics
.
Args | |
---|---|
train_step
|
(Optional) Step counter for training iterations. If None, no metric is generated against the global step. |
step_metrics
|
(Optional) Iterable of step metrics to generate summaries against. |
Returns | |
---|---|
A list of scalar summaries. |
__call__
__call__(
*args, **kwargs
)
Returns op to execute to update this metric for these inputs.
Returns None if eager execution is enabled. Returns a graph-mode function if graph execution is enabled.
Args | |
---|---|
*args
|
|
**kwargs
|
A mini-batch of inputs to the Metric, passed on to call() .
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