[RLLib] Small parallel iterator doc fix.
diff --git a/doc/source/rllib-concepts.rst b/doc/source/rllib-concepts.rst
index c35190a25b29c..db3e844838fdd 100644
--- a/doc/source/rllib-concepts.rst
+++ b/doc/source/rllib-concepts.rst
@@ -614,7 +614,7 @@ In code, this dataflow can be expressed as the following execution plan, which i
         return StandardMetricsReporting(train_op, workers, config)
 
 
-As you can see, each step returns an *iterator* over objects (if you're unfamiliar with distributed iterators, see Ray's `parallel iterators documentation <iter.html>`__). The reason it is a ``LocalIterator`` is that, though it is based on a parallel computation, the iterator has been turned into one that can be consumed locally in sequence by the program. A couple other points to note:
+As you can see, each step returns an *iterator* over objects (if you're unfamiliar with distributed iterators, see Ray's `parallel iterators implementation <https://github.com/ray-project/ray/blob/master/python/ray/util/iter.py>`__). The reason it is a ``LocalIterator`` is that, though it is based on a parallel computation, the iterator has been turned into one that can be consumed locally in sequence by the program. A couple other points to note:
 
  - The reason the plan returns an iterator over training results, is that ``trainer.train()`` is pulling results from this iterator to return as the result of the train call.
  - The rollout workers have been already created ahead of time in the ``WorkerSet``, so the execution plan function is only defining a sequence of operations over the results of the rollouts.
@@ -624,7 +624,7 @@ These iterators represent the infinite stream of data items that can be produced
 Understanding and Debugging Execution Plans
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Execution plans are based on Ray `parallel iterators <iter.html>`__ and can be inspected similarly. For example, suppose you wanted to print out the intermediate data items during training. This can be done by inserting a print function into the dataflow, e.g., for A2C:
+Execution plans are based on Ray `parallel iterators <https://github.com/ray-project/ray/blob/master/python/ray/util/iter.py>`__ and can be inspected similarly. For example, suppose you wanted to print out the intermediate data items during training. This can be done by inserting a print function into the dataflow, e.g., for A2C:
 
 .. code-block:: python
 

## Why are these changes needed?

/iter.html was removed when Ray Dataset documentations were introduced.
Pointing to the source code seems like the best option available at the point.

## Related issue number

N/A

## Checks

- [ ] I've run `scripts/format.sh` to lint the changes in this PR.
- [x] I've included any doc changes needed for https://docs.ray.io/en/master/.
- [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
   - [ ] Unit tests
   - [ ] Release tests
   - [ ] This PR is not tested :(
