[Doc] Getting started tutos (#1886)

README.md

@@ -33,6 +33,11 @@ On the low-level end, torchrl comes with a set of highly re-usable functionals f
 
 TorchRL aims at (1) a high modularity and (2) good runtime performance. Read the [full paper](https://arxiv.org/abs/2306.00577) for a more curated description of the library.
 
+## Getting started
+
+Check our [Getting Started tutorials](https://pytorch.org/rl/index.html#getting-started) for quickly ramp up with the basic 
+features of the library!
+
 ## Documentation and knowledge base
 
 The TorchRL documentation can be found [here](https://pytorch.org/rl).

docs/source/index.rst

@@ -62,6 +62,23 @@ or via a ``git clone`` if you're willing to contribute to the library:
   $ cd ../rl
   $ python setup.py develop
 
+Getting started
+===============
+
+A series of quick tutorials to get ramped up with the basic features of the
+library. If you're in a hurry, you can start by
+:ref:`the last item of the series <gs_first_training>`
+and navigate to the previous ones whenever you want to learn more!
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/getting-started-0
+   tutorials/getting-started-1
+   tutorials/getting-started-2
+   tutorials/getting-started-3
+   tutorials/getting-started-4
+   tutorials/getting-started-5
 
 Tutorials
 =========

docs/source/reference/collectors.rst

@@ -3,6 +3,8 @@
 torchrl.collectors package
 ==========================
 
+.. _ref_collectors:
+
 Data collectors are somewhat equivalent to pytorch dataloaders, except that (1) they
 collect data over non-static data sources and (2) the data is collected using a model
 (likely a version of the model that is being trained).

docs/source/reference/data.rst

@@ -3,6 +3,8 @@
 torchrl.data package
 ====================
 
+.. _ref_data:
+
 Replay Buffers
 --------------
 

docs/source/reference/envs.rst

@@ -475,6 +475,9 @@ single agent standards.
 
 Transforms
 ----------
+
+.. _transforms:
+
 .. currentmodule:: torchrl.envs.transforms
 
 In most cases, the raw output of an environment must be treated before being passed to another object (such as a

docs/source/reference/modules.rst

@@ -3,9 +3,13 @@
 torchrl.modules package
 =======================
 
+.. _ref_modules:
+
 TensorDict modules: Actors, exploration, value models and generative models
 ---------------------------------------------------------------------------
 
+.. _tdmodules:
+
 TorchRL offers a series of module wrappers aimed at making it easy to build
 RL models from the ground up. These wrappers are exclusively based on
 :class:`tensordict.nn.TensorDictModule` and :class:`tensordict.nn.TensorDictSequential`.

docs/source/reference/objectives.rst

@@ -3,6 +3,8 @@
 torchrl.objectives package
 ==========================
 
+.. _ref_objectives:
+
 TorchRL provides a series of losses to use in your training scripts.
 The aim is to have losses that are easily reusable/swappable and that have
 a simple signature.

docs/source/reference/trainers.rst

@@ -218,6 +218,8 @@ Utils
 Loggers
 -------
 
+.. _ref_loggers:
+
 .. currentmodule:: torchrl.record.loggers
 
 .. autosummary::

torchrl/data/replay_buffers/samplers.py

@@ -718,7 +718,7 @@ def __init__(
             if end_key is None:
                 end_key = ("next", "done")
             if traj_key is None:
-                traj_key = "run"
+                traj_key = "episode"
             self.end_key = end_key
             self.traj_key = traj_key
 

torchrl/envs/common.py

@@ -2055,8 +2055,8 @@ def reset(
 
         tensordict_reset = self._reset(tensordict, **kwargs)
         #        We assume that this is done properly
-        #        if tensordict_reset.device != self.device:
-        #            tensordict_reset = tensordict_reset.to(self.device, non_blocking=True)
+        #        if reset.device != self.device:
+        #            reset = reset.to(self.device, non_blocking=True)
         if tensordict_reset is tensordict:
             raise RuntimeError(
                 "EnvBase._reset should return outplace changes to the input "

torchrl/envs/transforms/transforms.py

@@ -791,7 +791,7 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs):
         return tensordict_reset
 
     def _reset_proc_data(self, tensordict, tensordict_reset):
-        # self._complete_done(self.full_done_spec, tensordict_reset)
+        # self._complete_done(self.full_done_spec, reset)
         self._reset_check_done(tensordict, tensordict_reset)
         if tensordict is not None:
             tensordict_reset = _update_during_reset(
@@ -802,7 +802,7 @@ def _reset_proc_data(self, tensordict, tensordict_reset):
         # # doesn't do anything special
         # mt_mode = self.transform.missing_tolerance
         # self.set_missing_tolerance(True)
-        # tensordict_reset = self.transform._call(tensordict_reset)
+        # reset = self.transform._call(reset)
         # self.set_missing_tolerance(mt_mode)
         return tensordict_reset
 

torchrl/modules/tensordict_module/actors.py

@@ -33,12 +33,13 @@
 class Actor(SafeModule):
     """General class for deterministic actors in RL.
 
-    The Actor class comes with default values for the out_keys (["action"])
-    and if the spec is provided but not as a CompositeSpec object, it will be
-    automatically translated into :obj:`spec = CompositeSpec(action=spec)`
+    The Actor class comes with default values for the out_keys (``["action"]``)
+    and if the spec is provided but not as a
+    :class:`~torchrl.data.CompositeSpec` object, it will be
+    automatically translated into ``spec = CompositeSpec(action=spec)``.
 
     Args:
-        module (nn.Module): a :class:`torch.nn.Module` used to map the input to
+        module (nn.Module): a :class:`~torch.nn.Module` used to map the input to
             the output parameter space.
         in_keys (iterable of str, optional): keys to be read from input
             tensordict and passed to the module. If it
@@ -47,9 +48,11 @@ class Actor(SafeModule):
             Defaults to ``["observation"]``.
         out_keys (iterable of str): keys to be written to the input tensordict.
             The length of out_keys must match the
-            number of tensors returned by the embedded module. Using "_" as a
+            number of tensors returned by the embedded module. Using ``"_"`` as a
             key avoid writing tensor to output.
             Defaults to ``["action"]``.
+
+    Keyword Args:
         spec (TensorSpec, optional): Keyword-only argument.
             Specs of the output tensor. If the module
             outputs multiple output tensors,
@@ -59,7 +62,7 @@ class Actor(SafeModule):
             input spec. Out-of-domain sampling can
             occur because of exploration policies or numerical under/overflow
             issues. If this value is out of bounds, it is projected back onto the
-            desired space using the :obj:`TensorSpec.project`
+            desired space using the :meth:`~torchrl.data.TensorSpec.project`
             method. Default is ``False``.
 
     Examples:
@@ -148,17 +151,23 @@ class ProbabilisticActor(SafeProbabilisticTensorDictSequential):
             issues. If this value is out of bounds, it is projected back onto the
             desired space using the :obj:`TensorSpec.project`
             method. Default is ``False``.
-        default_interaction_type=InteractionType.RANDOM (str, optional): keyword-only argument.
+        default_interaction_type (str, optional): keyword-only argument.
             Default method to be used to retrieve
-            the output value. Should be one of: 'mode', 'median', 'mean' or 'random'
-            (in which case the value is sampled randomly from the distribution). Default
-            is 'mode'.
-            Note: When a sample is drawn, the :obj:`ProbabilisticTDModule` instance will
-            first look for the interaction mode dictated by the `interaction_typ()`
-            global function. If this returns `None` (its default value), then the
-            `default_interaction_type` of the `ProbabilisticTDModule` instance will be
-            used. Note that DataCollector instances will use `set_interaction_type` to
-            :class:`tensordict.nn.InteractionType.RANDOM` by default.
+            the output value. Should be one of: 'InteractionType.MODE',
+            'InteractionType.MEDIAN', 'InteractionType.MEAN' or
+            'InteractionType.RANDOM' (in which case the value is sampled
+            randomly from the distribution). Defaults to is 'InteractionType.RANDOM'.
+
+            .. note:: When a sample is drawn, the :class:`ProbabilisticActor` instance will
+              first look for the interaction mode dictated by the
+              :func:`~tensordict.nn.probabilistic.interaction_type`
+              global function. If this returns `None` (its default value), then the
+              `default_interaction_type` of the `ProbabilisticTDModule`
+              instance will be used. Note that
+              :class:`~torchrl.collectors.collectors.DataCollectorBase`
+              instances will use `set_interaction_type` to
+              :class:`tensordict.nn.InteractionType.RANDOM` by default.
+
         distribution_class (Type, optional): keyword-only argument.
             A :class:`torch.distributions.Distribution` class to
             be used for sampling.
@@ -197,9 +206,7 @@ class ProbabilisticActor(SafeProbabilisticTensorDictSequential):
         ...    in_keys=["loc", "scale"],
         ...    distribution_class=TanhNormal,
         ...    )
-        >>> params = TensorDict.from_module(td_module)
-        >>> with params.to_module(td_module):
-        ...     td = td_module(td)
+        >>> td = td_module(td)
         >>> td
         TensorDict(
             fields={
@@ -315,7 +322,8 @@ class ValueOperator(TensorDictModule):
             The length of out_keys must match the
             number of tensors returned by the embedded module. Using "_" as a
             key avoid writing tensor to output.
-            Defaults to ``["action"]``.
+            Defaults to ``["state_value"]`` or
+            ``["state_action_value"]`` if ``"action"`` is part of the ``in_keys``.
 
     Examples:
         >>> import torch
@@ -334,9 +342,7 @@ class ValueOperator(TensorDictModule):
         >>> td_module = ValueOperator(
         ...    in_keys=["observation", "action"], module=module
         ... )
-        >>> params = TensorDict.from_module(td_module)
-        >>> with params.to_module(td_module):
-        ...     td = td_module(td)
+        >>> td = td_module(td)
         >>> print(td)
         TensorDict(
             fields={

torchrl/objectives/dqn.py

@@ -213,7 +213,10 @@ def __init__(
                 try:
                     action_space = value_network.action_space
                 except AttributeError:
-                    raise ValueError(self.ACTION_SPEC_ERROR)
+                    raise ValueError(
+                        "The action space could not be retrieved from the value_network. "
+                        "Make sure it is available to the DQN loss module."
+                    )
         if action_space is None:
             warnings.warn(
                 "action_space was not specified. DQNLoss will default to 'one-hot'."

torchrl/objectives/utils.py

@@ -300,8 +300,7 @@ def __init__(
     ):
         if eps is None and tau is None:
             raise RuntimeError(
-                "Neither eps nor tau was provided. " "This behaviour is deprecated.",
-                category=DeprecationWarning,
+                "Neither eps nor tau was provided. This behaviour is deprecated.",
             )
             eps = 0.999
         if (eps is None) ^ (tau is None):

torchrl/record/loggers/csv.py

@@ -2,6 +2,8 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
+from __future__ import annotations
+
 import os
 from collections import defaultdict
 from pathlib import Path
@@ -126,7 +128,7 @@ class CSVLogger(Logger):
     def __init__(
         self,
         exp_name: str,
-        log_dir: Optional[str] = None,
+        log_dir: str | None = None,
         video_format: str = "pt",
         video_fps: int = 30,
     ) -> None:

tutorials/sphinx-tutorials/README.rst

@@ -1,2 +1,4 @@
 README Tutos
 ============
+
+Check the tutorials on torchrl documentation: https://pytorch.org/rl

tutorials/sphinx-tutorials/coding_ddpg.py

@@ -4,6 +4,8 @@
 ======================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
+.. _coding_ddpg:
+
 """
 
 ##############################################################################

tutorials/sphinx-tutorials/coding_dqn.py

@@ -4,6 +4,8 @@
 ==============================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
+.. _coding_dqn:
+
 """
 
 ##############################################################################
@@ -404,9 +406,9 @@ def get_replay_buffer(buffer_size, n_optim, batch_size):
 # environment executed in parallel in each collector (controlled by the
 # ``num_workers`` hyperparameter).
 #
-# When building the collector, we can choose on which device we want the
-# environment and policy to execute the operations through the ``device``
-# keyword argument. The ``storing_devices`` argument will modify the
+# Collector's devices are fully parametrizable through the ``device`` (general),
+# ``policy_device``, ``env_device`` and ``storing_device`` arguments.
+# The ``storing_device`` argument will modify the
 # location of the data being collected: if the batches that we are gathering
 # have a considerable size, we may want to store them on a different location
 # than the device where the computation is happening. For asynchronous data

tutorials/sphinx-tutorials/coding_ppo.py

@@ -4,6 +4,8 @@
 ==================================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
+.. _coding_ppo:
+
 This tutorial demonstrates how to use PyTorch and :py:mod:`torchrl` to train a parametric policy
 network to solve the Inverted Pendulum task from the `OpenAI-Gym/Farama-Gymnasium
 control library <https://github.com/Farama-Foundation/Gymnasium>`__.

tutorials/sphinx-tutorials/dqn_with_rnn.py

@@ -6,6 +6,8 @@
 
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
+.. _RNN_tuto:
+
 .. grid:: 2
 
     .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn