changed the discount head to predict terminal

models.py

@@ -107,16 +107,15 @@ class WorldModel(nn.Module):
                 dist=config.reward_head,
                 outscale=0.0,
             )
-        if config.pred_discount:
-            self.heads["discount"] = networks.DenseHead(
-                feat_size,  # pytorch version
-                [],
-                config.discount_layers,
-                config.units,
-                config.act,
-                config.norm,
-                dist="binary",
-            )
+        self.heads["cont"] = networks.DenseHead(
+            feat_size,  # pytorch version
+            [],
+            config.cont_layers,
+            config.units,
+            config.act,
+            config.norm,
+            dist="binary",
+        )
         for name in config.grad_heads:
             assert name in self.heads, name
         self._model_opt = tools.Optimizer(
@@ -129,7 +128,7 @@ class WorldModel(nn.Module):
             opt=config.opt,
             use_amp=self._use_amp,
         )
-        self._scales = dict(reward=config.reward_scale, discount=config.discount_scale)
+        self._scales = dict(reward=config.reward_scale, cont=config.cont_scale)
 
     def _train(self, data):
         # action (batch_size, batch_length, act_dim)
@@ -143,10 +142,10 @@ class WorldModel(nn.Module):
                 embed = self.encoder(data)
                 post, prior = self.dynamics.observe(embed, data["action"])
                 kl_free = tools.schedule(self._config.kl_free, self._step)
-                kl_lscale = tools.schedule(self._config.kl_lscale, self._step)
-                kl_rscale = tools.schedule(self._config.kl_rscale, self._step)
-                kl_loss, kl_value, loss_lhs, loss_rhs = self.dynamics.kl_loss(
-                    post, prior, self._config.kl_forward, kl_free, kl_lscale, kl_rscale
+                dyn_scale = tools.schedule(self._config.dyn_scale, self._step)
+                rep_scale = tools.schedule(self._config.rep_scale, self._step)
+                kl_loss, kl_value, dyn_loss, rep_loss = self.dynamics.kl_loss(
+                    post, prior, kl_free, dyn_scale, rep_scale
                 )
                 losses = {}
                 likes = {}
@@ -163,10 +162,10 @@ class WorldModel(nn.Module):
 
         metrics.update({f"{name}_loss": to_np(loss) for name, loss in losses.items()})
         metrics["kl_free"] = kl_free
-        metrics["kl_lscale"] = kl_lscale
-        metrics["kl_rscale"] = kl_rscale
-        metrics["loss_lhs"] = to_np(loss_lhs)
-        metrics["loss_rhs"] = to_np(loss_rhs)
+        metrics["dyn_scale"] = dyn_scale
+        metrics["rep_scale"] = rep_scale
+        metrics["dyn_loss"] = to_np(dyn_loss)
+        metrics["rep_loss"] = to_np(rep_loss)
         metrics["kl"] = to_np(torch.mean(kl_value))
         with torch.cuda.amp.autocast(self._use_amp):
             metrics["prior_ent"] = to_np(
@@ -193,6 +192,11 @@ class WorldModel(nn.Module):
             obs["discount"] *= self._config.discount
             # (batch_size, batch_length) -> (batch_size, batch_length, 1)
             obs["discount"] = torch.Tensor(obs["discount"]).unsqueeze(-1)
+        if "is_terminal" in obs:
+            # this label is necessary to train cont_head
+            obs["cont"] = torch.Tensor(1.0 - obs["is_terminal"]).unsqueeze(-1)
+        else:
+            raise ValueError('"is_terminal" was not found in observation.')
         obs = {k: torch.Tensor(v).to(self._config.device) for k, v in obs.items()}
         return obs
 
@@ -347,7 +351,14 @@ class ImagBehavior(nn.Module):
         metrics.update(tools.tensorstats(value.mode(), "value"))
         metrics.update(tools.tensorstats(target, "target"))
         metrics.update(tools.tensorstats(reward, "imag_reward"))
-        metrics.update(tools.tensorstats(imag_action, "imag_action"))
+        if self._config.actor_dist in ["onehot"]:
+            metrics.update(
+                tools.tensorstats(
+                    torch.argmax(imag_action, dim=-1).float(), "imag_action"
+                )
+            )
+        else:
+            metrics.update(tools.tensorstats(imag_action, "imag_action"))
         metrics["actor_ent"] = to_np(torch.mean(actor_ent))
         with tools.RequiresGrad(self):
             metrics.update(self._actor_opt(actor_loss, self.actor.parameters()))
@@ -390,9 +401,9 @@ class ImagBehavior(nn.Module):
     def _compute_target(
         self, imag_feat, imag_state, imag_action, reward, actor_ent, state_ent
     ):
-        if "discount" in self._world_model.heads:
+        if "cont" in self._world_model.heads:
             inp = self._world_model.dynamics.get_feat(imag_state)
-            discount = self._world_model.heads["discount"](inp).mean
+            discount = self._config.discount * self._world_model.heads["cont"](inp).mean
         else:
             discount = self._config.discount * torch.ones_like(reward)
         if self._config.future_entropy and self._config.actor_entropy() > 0: