diff --git a/dreamer.py b/dreamer.py
index 12ef8a2..36eb633 100644
--- a/dreamer.py
+++ b/dreamer.py
@@ -55,10 +55,10 @@ class Dreamer(nn.Module):
         self._task_behavior = models.ImagBehavior(
             config, self._wm, config.behavior_stop_grad
         )
-        if config.compile:
+        if config.compile and os.name != 'nt':  # compilation is not supported on windows
             self._wm = torch.compile(self._wm)
             self._task_behavior = torch.compile(self._task_behavior)
-        reward = lambda f, s, a: self._wm.heads["reward"](f).mean
+        reward = lambda f, s, a: self._wm.heads["reward"](f).mean()
         self._expl_behavior = dict(
             greedy=lambda: self._task_behavior,
             random=lambda: expl.Random(config, act_space),
diff --git a/exploration.py b/exploration.py
index f195bb8..5eefbf9 100644
--- a/exploration.py
+++ b/exploration.py
@@ -58,7 +58,7 @@ class Plan2Explore(nn.Module):
             "feat": config.dyn_stoch + config.dyn_deter,
         }[self._config.disag_target]
         kw = dict(
-            inp_dim=feat_size,  # pytorch version
+            inp_dim=feat_size + config.num_actions if config.disag_action_cond else 0,  # pytorch version
             shape=size,
             layers=config.disag_layers,
             units=config.disag_units,
@@ -93,7 +93,7 @@ class Plan2Explore(nn.Module):
             }[self._config.disag_target]
             inputs = context["feat"]
             if self._config.disag_action_cond:
-                inputs = torch.concat([inputs, data["action"]], -1)
+                inputs = torch.concat([inputs, torch.Tensor(data["action"]).to(self._config.device)], -1)
             metrics.update(self._train_ensemble(inputs, target))
             metrics.update(self._behavior._train(start, self._intrinsic_reward)[-1])
         return None, metrics
@@ -110,10 +110,7 @@ class Plan2Explore(nn.Module):
             disag = torch.log(disag)
         reward = self._config.expl_intr_scale * disag
         if self._config.expl_extr_scale:
-            reward += torch.cast(
-                self._config.expl_extr_scale * self._reward(feat, state, action),
-                torch.float32,
-            )
+            reward += self._config.expl_extr_scale * self._reward(feat, state, action)
         return reward
 
     def _train_ensemble(self, inputs, targets):
diff --git a/networks.py b/networks.py
index 9c58faf..3a767fa 100644
--- a/networks.py
+++ b/networks.py
@@ -215,7 +215,7 @@ class RSSM(nn.Module):
                     is_first,
                     is_first.shape + (1,) * (len(val.shape) - len(is_first.shape)),
                 )
-                val = val * (1.0 - is_first_r) + init_state[key] * is_first_r
+                prev_state[key] = val * (1.0 - is_first_r) + init_state[key] * is_first_r
 
         prior = self.img_step(prev_state, prev_action, None, sample)
         if self._shared: