amend

pytorch · Apr 24, 2024 · e798882 · e798882
1 parent 515ae33
commit e798882
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Showing 5 changed files with 5 additions and 8 deletions.
diff --git a/tutorials/sphinx-tutorials/coding_dqn.py b/tutorials/sphinx-tutorials/coding_dqn.py
@@ -104,6 +104,7 @@
 
 try:
     multiprocessing.set_start_method("spawn" if is_sphinx else "fork")
+    mp_context = "fork"
 except RuntimeError:
     # If we can't set the method globally we can still run the parallel env with "fork"
     # This will fail on windows! Use "spawn" and put the script within `if __name__ == "__main__"`

diff --git a/tutorials/sphinx-tutorials/dqn_with_rnn.py b/tutorials/sphinx-tutorials/dqn_with_rnn.py
@@ -307,7 +307,7 @@
 # either by passing a string or an action-spec. This allows us to use
 # Categorical (sometimes called "sparse") encoding or the one-hot version of it.
 #
-qval = QValueModule(action_space=env.action_spec)
+qval = QValueModule(action_space=None, spec=env.action_spec)
 
 ######################################################################
 # .. note::

diff --git a/tutorials/sphinx-tutorials/getting-started-1.py b/tutorials/sphinx-tutorials/getting-started-1.py
@@ -273,9 +273,7 @@
 
 policy = TensorDictSequential(
     value_net,  # writes action values in our tensordict
-    QValueModule(
-        action_space=env.action_spec
-    ),  # Reads the "action_value" entry by default
+    QValueModule(spec=env.action_spec),  # Reads the "action_value" entry by default
 )
 
 ###################################

diff --git a/tutorials/sphinx-tutorials/getting-started-5.py b/tutorials/sphinx-tutorials/getting-started-5.py
@@ -66,7 +66,7 @@
 
 value_mlp = MLP(out_features=env.action_spec.shape[-1], num_cells=[64, 64])
 value_net = Mod(value_mlp, in_keys=["observation"], out_keys=["action_value"])
-policy = Seq(value_net, QValueModule(env.action_spec))
+policy = Seq(value_net, QValueModule(spec=env.action_spec))
 exploration_module = EGreedyModule(
     env.action_spec, annealing_num_steps=100_000, eps_init=0.5
 )

diff --git a/tutorials/sphinx-tutorials/torchrl_envs.py b/tutorials/sphinx-tutorials/torchrl_envs.py
@@ -145,9 +145,7 @@
 # we can just generate a random action:
 
 
-policy = TensorDictModule(
-    functools.partial(env.action_spec.rand, env=env), in_keys=[], out_keys=["action"]
-)
+policy = TensorDictModule(env.action_spec.rand, in_keys=[], out_keys=["action"])
 
 
 policy(reset_data)