Remove reset_buffer() from reset method (#501)

tianshou/data/buffer/base.py

@@ -96,7 +96,7 @@ class ReplayBuffer:
         """Load replay buffer from HDF5 file."""
         with h5py.File(path, "r") as f:
             buf = cls.__new__(cls)
-            buf.__setstate__(from_hdf5(f, device=device))
+            buf.__setstate__(from_hdf5(f, device=device))  # type: ignore
         return buf
 
     def reset(self, keep_statistics: bool = False) -> None:

tianshou/data/collector.py

@@ -46,6 +46,10 @@ class Collector(object):
 
         Please make sure the given environment has a time limitation if using n_episode
         collect option.
+
+    .. note::
+        In past versions of Tianshou, the replay buffer that was passed to `__init__`
+        was automatically reset. This is not done in the current implementation.
     """
 
     def __init__(
@@ -68,7 +72,7 @@ class Collector(object):
         self.preprocess_fn = preprocess_fn
         self._action_space = env.action_space
         # avoid creating attribute outside __init__
-        self.reset()
+        self.reset(False)
 
     def _assign_buffer(self, buffer: Optional[ReplayBuffer]) -> None:
         """Check if the buffer matches the constraint."""
@@ -94,14 +98,19 @@ class Collector(object):
                 )
         self.buffer = buffer
 
-    def reset(self) -> None:
+    def reset(self, reset_buffer: bool = True) -> None:
-        """Reset all related variables in the collector."""
+        """Reset the environment, statistics, current data and possibly replay memory.
+
+        :param bool reset_buffer: if true, reset the replay buffer that is attached
+            to the collector.
+        """
         # use empty Batch for "state" so that self.data supports slicing
         # convert empty Batch to None when passing data to policy
         self.data = Batch(
             obs={}, act={}, rew={}, done={}, obs_next={}, info={}, policy={}
         )
         self.reset_env()
+        if reset_buffer:
             self.reset_buffer()
         self.reset_stat()
 

tianshou/utils/net/continuous.py

@@ -48,7 +48,12 @@ class Actor(nn.Module):
         self.preprocess = preprocess_net
         self.output_dim = int(np.prod(action_shape))
         input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
-        self.last = MLP(input_dim, self.output_dim, hidden_sizes, device=self.device)
+        self.last = MLP(
+            input_dim,  # type: ignore
+            self.output_dim,
+            hidden_sizes,
+            device=self.device
+        )
         self._max = max_action
 
     def forward(
@@ -96,7 +101,12 @@ class Critic(nn.Module):
         self.preprocess = preprocess_net
         self.output_dim = 1
         input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
-        self.last = MLP(input_dim, 1, hidden_sizes, device=self.device)
+        self.last = MLP(
+            input_dim,  # type: ignore
+            1,
+            hidden_sizes,
+            device=self.device
+        )
 
     def forward(
         self,
@@ -165,11 +175,19 @@ class ActorProb(nn.Module):
         self.device = device
         self.output_dim = int(np.prod(action_shape))
         input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
-        self.mu = MLP(input_dim, self.output_dim, hidden_sizes, device=self.device)
+        self.mu = MLP(
+            input_dim,  # type: ignore
+            self.output_dim,
+            hidden_sizes,
+            device=self.device
+        )
         self._c_sigma = conditioned_sigma
         if conditioned_sigma:
             self.sigma = MLP(
-                input_dim, self.output_dim, hidden_sizes, device=self.device
+                input_dim,  # type: ignore
+                self.output_dim,
+                hidden_sizes,
+                device=self.device
             )
         else:
             self.sigma_param = nn.Parameter(torch.zeros(self.output_dim, 1))

tianshou/utils/net/discrete.py

@@ -49,7 +49,12 @@ class Actor(nn.Module):
         self.preprocess = preprocess_net
         self.output_dim = int(np.prod(action_shape))
         input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
-        self.last = MLP(input_dim, self.output_dim, hidden_sizes, device=self.device)
+        self.last = MLP(
+            input_dim,  # type: ignore
+            self.output_dim,
+            hidden_sizes,
+            device=self.device
+        )
         self.softmax_output = softmax_output
 
     def forward(
@@ -101,7 +106,12 @@ class Critic(nn.Module):
         self.preprocess = preprocess_net
         self.output_dim = last_size
         input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
-        self.last = MLP(input_dim, last_size, hidden_sizes, device=self.device)
+        self.last = MLP(
+            input_dim,  # type: ignore
+            last_size,
+            hidden_sizes,
+            device=self.device
+        )
 
     def forward(
         self, s: Union[np.ndarray, torch.Tensor], **kwargs: Any
@@ -183,8 +193,10 @@ class ImplicitQuantileNetwork(Critic):
         self.input_dim = getattr(
             preprocess_net, "output_dim", preprocess_net_output_dim
         )
-        self.embed_model = CosineEmbeddingNetwork(num_cosines,
+        self.embed_model = CosineEmbeddingNetwork(
-                                                  self.input_dim).to(device)
+            num_cosines,
+            self.input_dim  # type: ignore
+        ).to(device)
 
     def forward(  # type: ignore
         self, s: Union[np.ndarray, torch.Tensor], sample_size: int, **kwargs: Any