polish(nyz): polish code example yapf style

chapter1_overview/clip_grad_norm.py

@@ -8,8 +8,7 @@
 _tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
 
 
-def clip_grad_norm(
-        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
+def clip_grad_norm(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
     """
     **Overview**:
         Implementation of clip_grad_norm <link https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_ link>

chapter1_overview/clip_grad_norm_zh.py

@@ -8,8 +8,7 @@
 _tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
 
 
-def clip_grad_norm(
-        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
+def clip_grad_norm(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
     """
     **概述**:
         torch.nn.utils.clip_grad_norm 的 PyTorch 版实现。<link https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_ link>

chapter1_overview/ppo.py

@@ -13,7 +13,6 @@
 import torch
 import numpy as np
 
-
 ppo_policy_data = namedtuple('ppo_policy_data', ['logit_new', 'logit_old', 'action', 'adv', 'weight'])
 ppo_policy_loss = namedtuple('ppo_policy_loss', ['policy_loss', 'entropy_loss'])
 ppo_info = namedtuple('ppo_info', ['approx_kl', 'clipfrac'])

chapter1_overview/ppo_zh.py

@@ -15,7 +15,6 @@
 import torch
 import numpy as np
 
-
 ppo_policy_data = namedtuple('ppo_policy_data', ['logit_new', 'logit_old', 'action', 'adv', 'weight'])
 ppo_policy_loss = namedtuple('ppo_policy_loss', ['policy_loss', 'entropy_loss'])
 ppo_info = namedtuple('ppo_info', ['approx_kl', 'clipfrac'])

chapter2_action/continuous_tutorial.py

@@ -22,6 +22,7 @@
 
 
 class ContinuousPolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: int) -> None:
         """
         **Overview**:

chapter2_action/continuous_tutorial_zh.py

@@ -22,6 +22,7 @@
 
 
 class ContinuousPolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: int) -> None:
         """
         **ContinuousPolicyNetwork 定义概述**:

chapter2_action/discrete_tutorial.py

@@ -20,6 +20,7 @@
 
 
 class DiscretePolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: int) -> None:
         """
         **Overview**:

chapter2_action/discrete_tutorial_zh.py

@@ -20,6 +20,7 @@
 
 
 class DiscretePolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: int) -> None:
         """
         **DiscretePolicyNetwork 定义概述**:

chapter2_action/hybrid_tutorial.py

@@ -26,6 +26,7 @@
 
 
 class HybridPolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: Dict[str, int]) -> None:
         """
         **Overview**:
@@ -76,13 +77,7 @@ def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
         # $$\sigma = e^w$$
         sigma = torch.exp(log_sigma)
         # Return treetensor-type output.
-        return ttorch.as_tensor({
-            'action_type': logit,
-            'action_args': {
-                'mu': mu,
-                'sigma': sigma
-            }
-        })
+        return ttorch.as_tensor({'action_type': logit, 'action_args': {'mu': mu, 'sigma': sigma}})
 
 
 # delimiter

chapter2_action/hybrid_tutorial_zh.py

@@ -26,6 +26,7 @@
 
 
 class HybridPolicyNetwork(nn.Module):
+
     def __init__(self, obs_shape: int, action_shape: Dict[str, int]) -> None:
         """
         **HybridPolicyNetwork 概述**:
@@ -76,13 +77,7 @@ def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
         # $$\sigma = e^w$$
         sigma = torch.exp(log_sigma)
         # 返回 treetensor 类型的输出
-        return ttorch.as_tensor({
-            'action_type': logit,
-            'action_args': {
-                'mu': mu,
-                'sigma': sigma
-            }
-        })
+        return ttorch.as_tensor({'action_type': logit, 'action_args': {'mu': mu, 'sigma': sigma}})
 
 
 # delimiter

chapter3_obs/encoding.py

@@ -50,7 +50,7 @@ def get_binary_encoding(bit_num: int):
     # Generate a matrix with shape $$2^{B} \times B $$ where B is the bit_num.
     # Each row with index n contains the binary representation of n.
     location_embedding = []
-    for n in range(2**bit_num):
+    for n in range(2 ** bit_num):
         s = '0' * (bit_num - len(bin(n)[2:])) + bin(n)[2:]
         location_embedding.append(list(int(i) for i in s))
     mat = torch.FloatTensor(location_embedding)
@@ -74,12 +74,7 @@ def test_encoding():
     bin_enc = get_binary_encoding(2)
     x = torch.arange(4)
     y = bin_enc(x)
-    ground_truth = torch.LongTensor([
-        [0, 0],
-        [0, 1],
-        [1, 0],
-        [1, 1]
-    ])
+    ground_truth = torch.LongTensor([[0, 0], [0, 1], [1, 0], [1, 1]])
     assert torch.eq(y, ground_truth).all()
 
 

chapter3_obs/gradient.py

@@ -22,6 +22,7 @@ class LinearFunction(Function):
     **Overview**:
         Implementation of linear (Fully Connected) layer.
     """
+
     @staticmethod
     def forward(ctx, input_, weight, bias):
         """

chapter3_obs/mario_wrapper.py

@@ -19,6 +19,7 @@ class OpticalFlowWrapper(gym.Wrapper):
         Calculate optical flow using current frame and last frame. The final output contains one channel for current frame and two channels for optical flow.
         <link https://en.wikipedia.org/wiki/Optical_flow link>
     """
+
     def __init__(self, env):
         """
         **Overview**: