[Doc] Fix tutos

docs/source/conf.py

@@ -189,3 +189,8 @@
 generate_tutorial_references("../../tutorials/sphinx-tutorials/", "tutorial")
 # generate_tutorial_references("../../tutorials/src/", "src")
 generate_tutorial_references("../../tutorials/media/", "media")
+
+# We do this to indicate that the script is run by sphinx
+import builtins
+
+builtins.__sphinx_build__ = True

tutorials/sphinx-tutorials/coding_dqn.py

@@ -86,6 +86,8 @@
 import tempfile
 import warnings
 
+from tensordict.nn import TensorDictSequential
+
 warnings.filterwarnings("ignore")
 
 from torch import multiprocessing
@@ -94,13 +96,17 @@
 # `__main__` method call, but for the easy of reading the code switch to fork
 # which is also a default spawn method in Google's Colaboratory
 try:
-    multiprocessing.set_start_method("fork")
+    is_sphinx = __sphinx_build__
+except NameError:
+    is_sphinx = False
+
+try:
+    multiprocessing.set_start_method("spawn" if is_sphinx else "fork")
 except RuntimeError:
-    assert multiprocessing.get_start_method() == "fork"
+    pass
 
 
 # sphinx_gallery_end_ignore
-
 import os
 import uuid
 
@@ -125,7 +131,7 @@
     ToTensorImage,
     TransformedEnv,
 )
-from torchrl.modules import DuelingCnnDQNet, EGreedyWrapper, QValueActor
+from torchrl.modules import DuelingCnnDQNet, EGreedyModule, QValueActor
 
 from torchrl.objectives import DQNLoss, SoftUpdate
 from torchrl.record.loggers.csv import CSVLogger
@@ -270,6 +276,7 @@ def get_norm_stats():
     # let's check that normalizing constants have a size of ``[C, 1, 1]`` where
     # ``C=4`` (because of :class:`~torchrl.envs.CatFrames`).
     print("state dict of the observation norm:", obs_norm_sd)
+    test_env.close()
     return obs_norm_sd
 
 
@@ -328,13 +335,14 @@ def make_model(dummy_env):
     tensordict = dummy_env.fake_tensordict()
     actor(tensordict)
 
-    # we wrap our actor in an EGreedyWrapper for data collection
-    actor_explore = EGreedyWrapper(
-        actor,
+    # we join our actor with an EGreedyModule for data collection
+    exploration_module = EGreedyModule(
+        spec=dummy_env.action_spec,
         annealing_num_steps=total_frames,
         eps_init=eps_greedy_val,
         eps_end=eps_greedy_val_env,
     )
+    actor_explore = TensorDictSequential(actor, exploration_module)
 
     return actor, actor_explore
 
@@ -381,6 +389,13 @@ def get_replay_buffer(buffer_size, n_optim, batch_size):
 # We choose the following configuration: we will be running a series of
 # parallel environments synchronously in parallel in different collectors,
 # themselves running in parallel but asynchronously.
+#
+# .. note::
+#   This feature is only available when running the code within the "spawn"
+#   start method of python multiprocessing library. If this tutorial is run
+#   directly as a script (thereby using the "fork" method) we will be using
+#   a regular :class:`~torchrl.collectors.SyncDataCollector`.
+#
 # The advantage of this configuration is that we can balance the amount of
 # compute that is executed in batch with what we want to be executed
 # asynchronously. We encourage the reader to experiment how the collection
@@ -409,11 +424,10 @@ def get_collector(
     total_frames,
     device,
 ):
-    data_collector = MultiaSyncDataCollector(
-        [
-            make_env(parallel=True, obs_norm_sd=stats),
-        ]
-        * num_collectors,
+    cls = MultiaSyncDataCollector
+    env_arg = [make_env(parallel=True, obs_norm_sd=stats)] * num_collectors
+    data_collector = cls(
+        env_arg,
         policy=actor_explore,
         frames_per_batch=frames_per_batch,
         total_frames=total_frames,
@@ -464,7 +478,12 @@ def get_loss_module(actor, gamma):
 # in practice, and the performance of the algorithm should hopefully not be
 # too sensitive to slight variations of these.
 
-device = "cuda:0" if torch.cuda.device_count() > 0 else "cpu"
+is_fork = multiprocessing.get_start_method() == "fork"
+device = (
+    torch.device(0)
+    if torch.cuda.is_available() and not is_fork
+    else torch.device("cpu")
+)
 
 ###############################################################################
 # Optimizer
@@ -642,6 +661,12 @@ def get_loss_module(actor, gamma):
 )
 recorder.register(trainer)
 
+###############################################################################
+# The exploration module epsilon factor is also annealed:
+#
+
+trainer.register_op("post_steps", actor_explore[1].step, frames=frames_per_batch)
+
 ###############################################################################
 # - Any callable (including :class:`~torchrl.trainers.TrainerHookBase`
 #   subclasses) can be registered using :meth:`~torchrl.trainers.Trainer.register_op`.