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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
f4d1b1c
Update
svekars Jul 17, 2025
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14 changes: 10 additions & 4 deletions intermediate_source/dqn_with_rnn_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -342,7 +342,9 @@
# 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

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

######################################################################
# Because we still have a couple of uninitialized parameters we should
Expand Down Expand Up @@ -389,7 +391,10 @@
# 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 @@ -422,7 +427,8 @@
rb.extend(data.unsqueeze(0).to_tensordict().cpu())
for _ in range(utd):
s = rb.sample().to(device, non_blocking=True)
loss_vals = loss_fn(s)
with set_recurrent_mode(True):
loss_vals = loss_fn(s)
loss_vals["loss"].backward()
optim.step()
optim.zero_grad()
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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