reorder

Author: qianl-nv

docs/pages/example_workflows/reinforcement_learning/step_2_policy_training.rst

@@ -61,6 +61,47 @@ For example, to train with relu activation and a higher learning rate:
      agent.algorithm.learning_rate=0.001
 
 
+Resuming from a Checkpoint
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To resume training from a previously saved checkpoint, use the ``--resume`` flag
+together with ``--load_run`` (run folder name) and ``--checkpoint`` (model filename).
+Both arguments are optional — when omitted, the most recent run and latest checkpoint
+are used automatically.
+
+.. code-block:: bash
+
+   python submodules/IsaacLab/scripts/reinforcement_learning/rsl_rl/train.py \
+     --external_callback isaaclab_arena.environments.isaaclab_interop.environment_registration_callback \
+     --task lift_object \
+     --rl_training_mode \
+     --num_envs 4096 \
+     --max_iterations 4000 \
+     --resume \
+     --load_run <timestamp> \
+     --checkpoint model_1999.pt
+
+Replace ``<timestamp>`` with the run folder name under ``logs/rsl_rl/generic_experiment/``.
+If ``--load_run`` is omitted, the latest run is selected. If ``--checkpoint`` is omitted,
+the latest checkpoint in that run is loaded.
+
+.. tip::
+
+   You can also combine resume with Hydra overrides to change hyperparameters mid-training,
+   e.g. lowering the learning rate for fine-tuning:
+
+   .. code-block:: bash
+
+      python submodules/IsaacLab/scripts/reinforcement_learning/rsl_rl/train.py \
+        --external_callback isaaclab_arena.environments.isaaclab_interop.environment_registration_callback \
+        --task lift_object \
+        --rl_training_mode \
+        --num_envs 4096 \
+        --max_iterations 4000 \
+        --resume \
+        agent.algorithm.learning_rate=0.00005
+
+
 Monitoring Training
 ^^^^^^^^^^^^^^^^^^^
 
@@ -101,30 +142,6 @@ During training, each iteration prints a summary to the console:
                                      ETA: 00:00:49
 
 
-Resuming from a Checkpoint
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-To resume training from a previously saved checkpoint, use the ``--resume`` flag
-together with ``--load_run`` (run folder name) and ``--checkpoint`` (model filename).
-Both arguments are optional — when omitted, the most recent run and latest checkpoint
-are used automatically.
-
-.. code-block:: bash
-
-   python submodules/IsaacLab/scripts/reinforcement_learning/rsl_rl/train.py \
-     --external_callback isaaclab_arena.environments.isaaclab_interop.environment_registration_callback \
-     --task lift_object \
-     --rl_training_mode \
-     --num_envs 4096 \
-     --max_iterations 4000 \
-     --resume \
-     --load_run <timestamp> \
-     --checkpoint model_1999.pt
-
-Replace ``<timestamp>`` with the run folder name under ``logs/rsl_rl/generic_experiment/``.
-If ``--load_run`` is omitted, the latest run is selected. If ``--checkpoint`` is omitted,
-the latest checkpoint in that run is loaded.
-
 
 Multi-GPU Training
 ^^^^^^^^^^^^^^^^^^