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Fix typo (#316)
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* Fix typo

* Small wording improvements/suggestions

* Update evaluators.py

* retrigger checks
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asmith26 committed Aug 9, 2023
1 parent fc739c7 commit adca5f4
Showing 1 changed file with 10 additions and 10 deletions.
20 changes: 10 additions & 10 deletions d3rlpy/metrics/evaluators.py
Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ def make_batches(
class TDErrorEvaluator(EvaluatorProtocol):
r"""Returns average TD error.
This metics suggests how Q functions overfit to training sets.
This metric suggests how Q functions overfit to training sets.
If the TD error is large, the Q functions are overfitting.
.. math::
Expand Down Expand Up @@ -124,7 +124,7 @@ def __call__(
class DiscountedSumOfAdvantageEvaluator(EvaluatorProtocol):
r"""Returns average of discounted sum of advantage.
This metrics suggests how the greedy-policy selects different actions in
This metric suggests how the greedy-policy selects different actions in
action-value space.
If the sum of advantage is small, the policy selects actions with larger
estimated action-values.
Expand Down Expand Up @@ -191,7 +191,7 @@ def __call__(
class AverageValueEstimationEvaluator(EvaluatorProtocol):
r"""Returns average value estimation.
This metrics suggests the scale for estimation of Q functions.
This metric suggests the scale for estimation of Q functions.
If average value estimation is too large, the Q functions overestimate
action-values, which possibly makes training failed.
Expand Down Expand Up @@ -228,7 +228,7 @@ def __call__(
class InitialStateValueEstimationEvaluator(EvaluatorProtocol):
r"""Returns mean estimated action-values at the initial states.
This metrics suggests how much return the trained policy would get from
This metric suggests how much return the trained policy would get from
the initial states by deploying the policy to the states.
If the estimated value is large, the trained policy is expected to get
higher returns.
Expand Down Expand Up @@ -273,7 +273,7 @@ def __call__(
class SoftOPCEvaluator(EvaluatorProtocol):
r"""Returns Soft Off-Policy Classification metrics.
The metrics of the scorer funciton is evaluating gaps of action-value
The metric of the scorer funciton is evaluating gaps of action-value
estimation between the success episodes and the all episodes.
If the learned Q-function is optimal, action-values in success episodes
are expected to be higher than the others.
Expand Down Expand Up @@ -329,7 +329,7 @@ def __call__(
class ContinuousActionDiffEvaluator(EvaluatorProtocol):
r"""Returns squared difference of actions between algorithm and dataset.
This metrics suggests how different the greedy-policy is from the given
This metric suggests how different the greedy-policy is from the given
episodes in continuous action-space.
If the given episodes are near-optimal, the small action difference would
be better.
Expand Down Expand Up @@ -367,7 +367,7 @@ def __call__(
class DiscreteActionMatchEvaluator(EvaluatorProtocol):
r"""Returns percentage of identical actions between algorithm and dataset.
This metrics suggests how different the greedy-policy is from the given
This metric suggests how different the greedy-policy is from the given
episodes in discrete action-space.
If the given episdoes are near-optimal, the large percentage would be
better.
Expand Down Expand Up @@ -406,7 +406,7 @@ def __call__(
class CompareContinuousActionDiffEvaluator(EvaluatorProtocol):
r"""Action difference between algorithms.
This metrics suggests how different the two algorithms are in continuous
This metric suggests how different the two algorithms are in continuous
action-space.
If the algorithm to compare with is near-optimal, the small action
difference would be better.
Expand Down Expand Up @@ -454,7 +454,7 @@ def __call__(
class CompareDiscreteActionMatchEvaluator(EvaluatorProtocol):
r"""Action matches between algorithms.
This metrics suggests how different the two algorithms are in discrete
This metric suggests how different the two algorithms are in discrete
action-space.
If the algorithm to compare with is near-optimal, the small action
difference would be better.
Expand Down Expand Up @@ -501,7 +501,7 @@ def __call__(
class EnvironmentEvaluator(EvaluatorProtocol):
r"""Action matches between algorithms.
This metrics suggests how different the two algorithms are in discrete
This metric suggests how different the two algorithms are in discrete
action-space.
If the algorithm to compare with is near-optimal, the small action
difference would be better.
Expand Down

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