diff --git a/lerobot/common/envs/pusht.py b/lerobot/common/envs/pusht.py
index cf890046..45cbc705 100644
--- a/lerobot/common/envs/pusht.py
+++ b/lerobot/common/envs/pusht.py
@@ -44,6 +44,9 @@ class PushtEnv(EnvBase):
         if not _has_gym:
             raise ImportError("Cannot import gym.")
 
+        # TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
+        # from diffusion_policy.env.pusht.pusht_env import PushTEnv
+
         if not from_pixels:
             raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
         from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
diff --git a/lerobot/common/policies/tdmpc.py b/lerobot/common/policies/tdmpc.py
index 76a7b9aa..374c4089 100644
--- a/lerobot/common/policies/tdmpc.py
+++ b/lerobot/common/policies/tdmpc.py
@@ -1,3 +1,5 @@
+# ruff: noqa: N806
+
 from copy import deepcopy
 
 import einops
@@ -78,7 +80,7 @@ class TOLD(nn.Module):
             return torch.stack([q(x) for q in self._Qs], dim=0)
 
         idxs = np.random.choice(self.cfg.num_q, 2, replace=False)
-        Q1, Q2 = self._Qs[idxs[0]](x), self._Qs[idxs[1]](x)  # noqa: N806
+        Q1, Q2 = self._Qs[idxs[0]](x), self._Qs[idxs[1]](x)
         return torch.min(Q1, Q2) if return_type == "min" else (Q1 + Q2) / 2
 
 
@@ -146,21 +148,21 @@ class TDMPC(nn.Module):
     @torch.no_grad()
     def estimate_value(self, z, actions, horizon):
         """Estimate value of a trajectory starting at latent state z and executing given actions."""
-        G, discount = 0, 1  # noqa: N806
+        G, discount = 0, 1
         for t in range(horizon):
             if self.cfg.uncertainty_cost > 0:
-                G -= (  # noqa: N806
+                G -= (
                     discount
                     * self.cfg.uncertainty_cost
                     * self.model.Q(z, actions[t], return_type="all").std(dim=0)
                 )
             z, reward = self.model.next(z, actions[t])
-            G += discount * reward  # noqa: N806
+            G += discount * reward
             discount *= self.cfg.discount
         pi = self.model.pi(z, self.cfg.min_std)
-        G += discount * self.model.Q(z, pi, return_type="min")  # noqa: N806
+        G += discount * self.model.Q(z, pi, return_type="min")
         if self.cfg.uncertainty_cost > 0:
-            G -= discount * self.cfg.uncertainty_cost * self.model.Q(z, pi, return_type="all").std(dim=0)  # noqa: N806
+            G -= discount * self.cfg.uncertainty_cost * self.model.Q(z, pi, return_type="all").std(dim=0)
         return G
 
     @torch.no_grad()