unified the place to initialize the latents

dreamer.py

@@ -59,15 +59,6 @@ class Dreamer(nn.Module):
 
     def __call__(self, obs, reset, state=None, training=True):
         step = self._step
-        if self._should_reset(step):
-            state = None
-        if state is not None and reset.any():
-            mask = 1 - reset
-            for key in state[0].keys():
-                for i in range(state[0][key].shape[0]):
-                    state[0][key][i] *= mask[i]
-            for i in range(len(state[1])):
-                state[1][i] *= mask[i]
         if training:
             steps = (
                 self._config.pretrain
@@ -96,11 +87,7 @@ class Dreamer(nn.Module):
 
     def _policy(self, obs, state, training):
         if state is None:
-            batch_size = len(obs["image"])
+            latent = action = None
-            latent = self._wm.dynamics.initial(len(obs["image"]))
-            action = torch.zeros((batch_size, self._config.num_actions)).to(
-                self._config.device
-            )
         else:
             latent, action = state
         obs = self._wm.preprocess(obs)

models.py

@@ -202,7 +202,7 @@ class WorldModel(nn.Module):
         ]
         reward_post = self.heads["reward"](self.dynamics.get_feat(states)).mode()[:6]
         init = {k: v[:, -1] for k, v in states.items()}
-        prior = self.dynamics.imagine(data["action"][:6, 5:], init)
+        prior = self.dynamics.imagine_with_action(data["action"][:6, 5:], init)
         openl = self.heads["decoder"](self.dynamics.get_feat(prior))["image"].mode()
         reward_prior = self.heads["reward"](self.dynamics.get_feat(prior)).mode()
         # observed image is given until 5 steps

networks.py

@@ -51,6 +51,7 @@ class RSSM(nn.Module):
         self._temp_post = temp_post
         self._unimix_ratio = unimix_ratio
         self._initial = initial
+        self._num_actions = num_actions
         self._embed = embed
         self._device = device
 
@@ -151,8 +152,6 @@ class RSSM(nn.Module):
 
     def observe(self, embed, action, is_first, state=None):
         swap = lambda x: x.permute([1, 0] + list(range(2, len(x.shape))))
-        if state is None:
-            state = self.initial(action.shape[0])
         # (batch, time, ch) -> (time, batch, ch)
         embed, action, is_first = swap(embed), swap(action), swap(is_first)
         # prev_state[0] means selecting posterior of return(posterior, prior) from obs_step
@@ -169,10 +168,8 @@ class RSSM(nn.Module):
         prior = {k: swap(v) for k, v in prior.items()}
         return post, prior
 
-    def imagine(self, action, state=None):
+    def imagine_with_action(self, action, state):
         swap = lambda x: x.permute([1, 0] + list(range(2, len(x.shape))))
-        if state is None:
-            state = self.initial(action.shape[0])
         assert isinstance(state, dict), state
         action = action
         action = swap(action)
@@ -206,7 +203,14 @@ class RSSM(nn.Module):
         # otherwise, post use different network(_obs_out_layers) with prior[deter] and embed as inputs
         prev_action *= (1.0 / torch.clip(torch.abs(prev_action), min=1.0)).detach()
 
-        if torch.sum(is_first) > 0:
+        # initialize all prev_state
+        if prev_state == None or torch.sum(is_first) == len(is_first):
+            prev_state = self.initial(len(is_first))
+            prev_action = torch.zeros((len(is_first), self._num_actions)).to(
+                self._device
+            )
+        # overwrite the prev_state only where is_first=True
+        elif torch.sum(is_first) > 0:
             is_first = is_first[:, None]
             prev_action *= 1.0 - is_first
             init_state = self.initial(len(is_first))