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[Feature] SAC compatibility with composite distributions. #2447

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merged 8 commits into from
Oct 11, 2024
Merged

[Feature] SAC compatibility with composite distributions. #2447

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99e845c
tests sac composite dist
albertbou92 Sep 22, 2024
1bfaf6c
test fixes
albertbou92 Sep 22, 2024
c936df8
test fixes
albertbou92 Sep 22, 2024
cdb91ec
test fixes
albertbou92 Sep 22, 2024
9e5eb09
test fixes
albertbou92 Sep 22, 2024
c8c9cc8
test fixes
albertbou92 Sep 22, 2024
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albertbou92 Sep 22, 2024
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fix
albertbou92 Sep 22, 2024
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test fixes
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@albertbou92 @vmoens
albertbou92 authored and vmoens committed Oct 10, 2024
commit c8c9cc8c6905d89f7dfbee9cb170990203e301cd
19 changes: 13 additions & 6 deletions test/test_cost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3541,6 +3541,18 @@ def _create_mock_common_layer_setup(
n_hidden=2,
composite_action_dist=False,
):
class QValueClass(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(n_hidden + n_act, n_hidden)
self.relu = nn.ReLU()
self.linear2 = nn.Linear(n_hidden, 1)

def forward(self, obs, act):
if isinstance(act, TensorDictBase):
act = act.get("action1")
return self.linear2(self.relu(self.linear1(torch.cat([obs, act], -1))))

common = MLP(
num_cells=ncells,
in_features=n_obs,
Expand All @@ -3553,12 +3565,7 @@ def _create_mock_common_layer_setup(
depth=1,
out_features=2 * n_act,
)
qvalue = MLP(
in_features=n_hidden + n_act,
num_cells=ncells,
depth=1,
out_features=1,
)
qvalue = QValueClass()
batch = [batch]
action = torch.randn(*batch, n_act)
td = TensorDict(
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4 changes: 1 addition & 3 deletions torchrl/objectives/sac.py
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Expand Up @@ -51,9 +51,7 @@ def compute_log_prob(action_dist, action_or_tensordict, tensor_key):
if isinstance(action_or_tensordict, torch.Tensor):
log_p = action_dist.log_prob(action_or_tensordict)
else:
tensordict = action_dist.log_prob(action_or_tensordict)
log_p = tensordict.get(tensor_key)
maybe_log_prob = action_dist.log_prob(tensordict)
maybe_log_prob = action_dist.log_prob(action_or_tensordict)
if not isinstance(maybe_log_prob, torch.Tensor):
log_p = maybe_log_prob.get(tensor_key)
else:
Expand Down