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Fix DQN w RNN tutorial #3462

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Fix DQN w RNN tutorial #3462

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92e1abc
Fix DQN w RNN tutorial
svekars Jul 14, 2025
5e85c0a
Update intermediate_source/dqn_with_rnn_tutorial.py
svekars Jul 14, 2025
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12 changes: 9 additions & 3 deletions intermediate_source/dqn_with_rnn_tutorial.py
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Expand Up @@ -342,7 +342,10 @@
# will return a new instance of the LSTM (with shared weights) that will
# assume that the input data is sequential in nature.
#
policy = Seq(feature, lstm.set_recurrent_mode(True), mlp, qval)
from torchrl.modules import set_recurrent_mode

with set_recurrent_mode(True):
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This is to be set during the loss computation.

The policy module given to the collector and to the loss will be the same. The decorator is to be set to True whenever a recurrent call is required (typically within the loss).

policy = Seq(feature, lstm, mlp, qval)

######################################################################
# Because we still have a couple of uninitialized parameters we should
Expand Down Expand Up @@ -389,7 +392,10 @@
# For the sake of efficiency, we're only running a few thousands iterations
# here. In a real setting, the total number of frames should be set to 1M.
#
collector = SyncDataCollector(env, stoch_policy, frames_per_batch=50, total_frames=200, device=device)

collector = SyncDataCollector(
env, stoch_policy, frames_per_batch=50, total_frames=200, device=device
)
rb = TensorDictReplayBuffer(
storage=LazyMemmapStorage(20_000), batch_size=4, prefetch=10
)
Expand Down Expand Up @@ -464,5 +470,5 @@
#
# Further Reading
# ---------------
#
#
# - The TorchRL documentation can be found `here <https://pytorch.org/rl/>`_.
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