[RLlib] Fix broken links in docs. (ray-project#25013)

doc/source/rllib/core-concepts.rst

@@ -320,7 +320,7 @@ Examples
             return StandardMetricsReporting(train_op, workers, config)
 
 
-    Note that here we set ``output_indexes=[1]`` for the ``Concurrently`` operator, which makes it only return results from the replay op. See also the `DQN implementation of replay <https://github.com/ray-project/ray/blob/master/rllib/agents/dqn/dqn.py>`__ for a complete example including the implementation of options such as *training intensity*.
+    Note that here we set ``output_indexes=[1]`` for the ``Concurrently`` operator, which makes it only return results from the replay op. See also the `DQN implementation of replay <https://github.com/ray-project/ray/blob/master/rllib/algorithms/dqn/dqn.py>`__ for a complete example including the implementation of options such as *training intensity*.
 
 
 .. dropdown:: **Example: Multi-agent**

doc/source/rllib/package_ref/trainer.rst

@@ -44,7 +44,7 @@ and override one or more of its methods. Those are in particular:
 * :py:meth:`~ray.rllib.agents.trainer.Trainer.step_attempt`
 * :py:meth:`~ray.rllib.agents.trainer.Trainer.execution_plan`
 
-`See here for an example on how to override Trainer <https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg.py>`_.
+`See here for an example on how to override Trainer <https://github.com/ray-project/ray/blob/master/rllib/algorithms/pg/pg.py>`_.
 
 
 Trainer base class (ray.rllib.agents.trainer.Trainer)

doc/source/rllib/rllib-concepts.rst

@@ -9,8 +9,8 @@ This page describes the internal concepts used to implement algorithms in RLlib.
 You might find this useful if modifying or adding new algorithms to RLlib.
 
 Policy classes encapsulate the core numerical components of RL algorithms.
-This typically includes the policy model that determines actions to take, a trajectory postprocessor for experiences, and a loss function to improve the policy given postprocessed experiences.
-For a simple example, see the policy gradients `policy definition <https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg_tf_policy.py>`__.
+This typically includes the policy model that determines actions to take, a trajectory postprocessor for experiences, and a loss function to improve the policy given post-processed experiences.
+For a simple example, see the policy gradients `policy definition <https://github.com/ray-project/ray/blob/master/rllib/algorithms/pg/pg_tf_policy.py>`__.
 
 Most interaction with deep learning frameworks is isolated to the `Policy interface <https://github.com/ray-project/ray/blob/master/rllib/policy/policy.py>`__, allowing RLlib to support multiple frameworks.
 To simplify the definition of policies, RLlib includes `Tensorflow <#building-policies-in-tensorflow>`__ and `PyTorch-specific <#building-policies-in-pytorch>`__ templates.
@@ -375,7 +375,7 @@ In PPO we run ``setup_mixins`` before the loss function is called (i.e., ``befor
 
 **Example 2: Deep Q Networks**
 
-Let's look at how to implement a different family of policies, by looking at the `SimpleQ policy definition <https://github.com/ray-project/ray/blob/master/rllib/agents/dqn/simple_q_tf_policy.py>`__:
+Let's look at how to implement a different family of policies, by looking at the `SimpleQ policy definition <https://github.com/ray-project/ray/blob/master/rllib/algorithms/dqn/simple_q_tf_policy.py>`__:
 
 .. code-block:: python