fix(nyz): fix mlp dropout if condition bug

Author: PaParaZz1

ding/policy/dqn.py

@@ -41,7 +41,7 @@ class DQNPolicy(Policy):
         8  | ``model.dueling``   bool     True           | dueling head architecture
         9  | ``model.encoder``   list     [32, 64,       | Sequence of ``hidden_size`` of        | default kernel_size
            | ``_hidden``         (int)    64, 128]       | subsequent conv layers and the        | is [8, 4, 3]
-           | ``_size_list``                               | final dense layer.                   | default stride is
+           | ``_size_list``                              | final dense layer.                    | default stride is
                                                                                                  | [4, 2 ,1]
         10 | ``model.dropout``   float    None           | Dropout rate for dropout layers.      | [0,1]
                                                                                                  | If set to ``None``

ding/policy/mbpolicy/mbsac.py

@@ -1,15 +1,13 @@
 from typing import Dict, Any, List
 from functools import partial
-import copy
 
 import torch
 from torch import Tensor
 from torch import nn
-from torch.distributions import Normal, Independent, TransformedDistribution, TanhTransform
-from easydict import EasyDict
+from torch.distributions import Normal, Independent
 
 from ding.torch_utils import to_device, fold_batch, unfold_batch, unsqueeze_repeat
-from ding.utils import POLICY_REGISTRY, deep_merge_dicts
+from ding.utils import POLICY_REGISTRY
 from ding.policy import SACPolicy
 from ding.rl_utils import generalized_lambda_returns
 from ding.policy.common_utils import default_preprocess_learn
@@ -33,11 +31,12 @@ class MBSACPolicy(SACPolicy):
         == ====================   ========    =============  ==================================
         1  ``learn._lambda``      float       0.8            | Lambda for TD-lambda return.
         2  ``learn.grad_clip`     float       100.0          | Max norm of gradients.
-        3  ``learn.sample_``      bool        True           | Whether to sample states or tra-
-           ``state``                                         |   nsitions from env buffer.
+        3  | ``learn.sample``     bool        True           | Whether to sample states or
+           | ``_state``                                      | transitions from env buffer.
         == ====================   ========    =============  ==================================
 
     .. note::
+
         For other configs, please refer to ding.policy.sac.SACPolicy.
     """
 

ding/torch_utils/network/nn_module.py

@@ -396,7 +396,7 @@ def MLP(
         # The last layer uses the same activation as front layers.
         if activation is not None:
             block.append(activation)
-        if use_dropout is not None:
+        if use_dropout:
             block.append(nn.Dropout(dropout_probability))
 
     if last_linear_layer_init_zero: