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 ## Release 1.1.0
 
 ### Api Extensions
-- Batch received two new methods: `to_dict` and `to_list_of_dicts`. #1063
-- `Collector`s can now be closed, and their reset is more granular. #1063
-- Trainers can control whether collectors should be reset prior to training. #1063
-- Convenience constructor for `CollectStats` called `with_autogenerated_stats`. #1063
-- `SamplingConfig` supports `batch_size=None`. #1077
-- Batch received new methods: `to_numpy_` and `to_torch_`. #1098, #1117
-- `to_dict` in Batch supports also non-recursive conversion. #1098
-- Batch `__eq__` implemented, semantic equality check of batches is now possible. #1098
+- `data`:
+  - `Batch`:
+    - Add methods `to_dict` and `to_list_of_dicts`. #1063 #1098
+    - Add methods `to_numpy_` and `to_torch_`. #1098, #1117
+    - Add `__eq__` (semantic equality check). #1098
+  - `data.collector`:
+    - `Collector`:
+      - Add method `close` #1063
+      - Method `reset` is now more granular (new flags controlling behavior). #1063
+    - `CollectStats`: Add convenience constructor `with_autogenerated_stats`. #1063
+- `trainer`:
+  - Trainers can now control whether collectors should be reset prior to training. #1063
 - `Batch.keys()` deprecated in favor of `Batch.get_keys()` (needed to make iteration consistent with naming) #1105.
-- `highlevel.experiment`: 
-   - `Experiment` now has a `name` attribute, which can be set using `ExperimentBuilder.with_name` and 
-     which determines the default run name and therefore the persistence subdirectory.
-     It can still be overridden in `Experiment.run()`, the new parameter name being `run_name` rather than
-     `experiment_name` (although the latter will still be interpreted correctly). #1074 #1131
-   - Add class `ExperimentCollection` for the convenient execution of multiple experiment runs #1131
-   - `ExperimentBuilder`: 
-       - Add method `build_seeded_collection` for the sound creation of multiple
-         experiments with varying random seeds #1131
-       - Add method `copy` to facilitate the creation of multiple experiments from a single builder #1131
-- `SamplingConfig` has an explicit training seed, `test_seed` is inferred. #1074
-- New `evaluation` package for repeating the same experiment with multiple seeds and aggregating the results (important extension!). 
-Launchers for parallelization currently in alpha state. #1074
+- `highlevel`:
+  - `SamplingConfig`:
+    - Add support for `batch_size=None`. #1077 
+    - Add `training_seed` for explicit seeding of training and test environments, the `test_seed` is inferred from `training_seed`. #1074
+  - `highlevel.experiment`: 
+     - `Experiment` now has a `name` attribute, which can be set using `ExperimentBuilder.with_name` and 
+       which determines the default run name and therefore the persistence subdirectory.
+       It can still be overridden in `Experiment.run()`, the new parameter name being `run_name` rather than
+       `experiment_name` (although the latter will still be interpreted correctly). #1074 #1131
+     - Add class `ExperimentCollection` for the convenient execution of multiple experiment runs #1131
+     - `ExperimentBuilder`: 
+         - Add method `build_seeded_collection` for the sound creation of multiple
+           experiments with varying random seeds #1131
+         - Add method `copy` to facilitate the creation of multiple experiments from a single builder #1131
+- `evaluation`: New package for repeating the same experiment with multiple seeds and aggregating the results. #1074
+  - The module `evaluation.launchers` for parallelization is currently in alpha state. 
 - Loggers can now restore the logged data into python by using the new `restore_logged_data` method. #1074
-- `continuous.Critic`:
-  - Add flag `apply_preprocess_net_to_obs_only` to allow the
-    preprocessing network to be applied to the observations only (without
-    the actions concatenated), which is essential for the case where we want
-    to reuse the actor's preprocessing network #1128
+- `utils.net`:
+  - `continuous.Critic`:
+    - Add flag `apply_preprocess_net_to_obs_only` to allow the
+      preprocessing network to be applied to the observations only (without
+      the actions concatenated), which is essential for the case where we want
+      to reuse the actor's preprocessing network #1128
 
 ### Fixes
 - `CriticFactoryReuseActor`: Enable the Critic flag `apply_preprocess_net_to_obs_only` for continuous critics,