erased unused options

dreamer.py

@@ -42,9 +42,7 @@ class Dreamer(nn.Module):
         self._update_count = 0
         self._dataset = dataset
         self._wm = models.WorldModel(obs_space, act_space, self._step, config)
-        self._task_behavior = models.ImagBehavior(
-            config, self._wm, config.behavior_stop_grad
-        )
+        self._task_behavior = models.ImagBehavior(config, self._wm)
         if (
             config.compile and os.name != "nt"
         ):  # compilation is not supported on windows
@@ -92,9 +90,7 @@ class Dreamer(nn.Module):
             latent, action = state
         obs = self._wm.preprocess(obs)
         embed = self._wm.encoder(obs)
-        latent, _ = self._wm.dynamics.obs_step(
-            latent, action, embed, obs["is_first"], self._config.collect_dyn_sample
-        )
+        latent, _ = self._wm.dynamics.obs_step(latent, action, embed, obs["is_first"])
         if self._config.eval_state_mean:
             latent["stoch"] = latent["mean"]
         feat = self._wm.dynamics.get_feat(latent)
@@ -114,21 +110,10 @@ class Dreamer(nn.Module):
             action = torch.one_hot(
                 torch.argmax(action, dim=-1), self._config.num_actions
             )
-        action = self._exploration(action, training)
         policy_output = {"action": action, "logprob": logprob}
         state = (latent, action)
         return policy_output, state
 
-    def _exploration(self, action, training):
-        amount = self._config.expl_amount if training else self._config.eval_noise
-        if amount == 0:
-            return action
-        if "onehot" in self._config.actor["dist"]:
-            probs = amount / self._config.num_actions + (1 - amount) * action
-            return tools.OneHotDist(probs=probs).sample()
-        else:
-            return torch.clip(torchd.normal.Normal(action, amount).sample(), -1, 1)
-
     def _train(self, data):
         metrics = {}
         post, context, mets = self._wm._train(data)