diff --git a/examples/replay-buffers/filter-imcomplete-trajs.py b/examples/replay-buffers/filter-imcomplete-trajs.py
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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Efficient Trajectory Sampling with CompletedTrajRepertoire
+
+This example demonstrates how to design a custom transform that filters trajectories during sampling,
+ensuring that only completed trajectories are present in sampled batches. This can be particularly useful
+when dealing with environments where some trajectories might be corrupted or never reach a done state,
+which could skew the learning process or lead to biased models. For instance, in robotics or autonomous
+driving, a trajectory might be interrupted due to external factors such as hardware failures or human
+intervention, resulting in incomplete or inconsistent data. By filtering out these incomplete trajectories,
+we can improve the quality of the training data and increase the robustness of our models.
+"""
+
+import torch
+from tensordict import TensorDictBase
+from torchrl.data import LazyTensorStorage, ReplayBuffer
+from torchrl.envs import GymEnv, TrajCounter, Transform
+
+
+class CompletedTrajectoryRepertoire(Transform):
+    """
+    A transform that keeps track of completed trajectories and filters them out during sampling.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.completed_trajectories = set()
+        self.repertoire_tensor = torch.zeros((), dtype=torch.int64)
+
+    def _update_repertoire(self, tensordict: TensorDictBase) -> None:
+        """Updates the repertoire of completed trajectories."""
+        done = tensordict["next", "terminated"].squeeze(-1)
+        traj = tensordict["next", "traj_count"][done].view(-1)
+        if traj.numel():
+            self.completed_trajectories = self.completed_trajectories.union(
+                traj.tolist()
+            )
+            self.repertoire_tensor = torch.tensor(
+                list(self.completed_trajectories), dtype=torch.int64
+            )
+
+    def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
+        """Updates the repertoire of completed trajectories during insertion."""
+        self._update_repertoire(tensordict)
+        return tensordict
+
+    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+        """Filters out incomplete trajectories during sampling."""
+        traj = tensordict["next", "traj_count"]
+        traj = traj.unsqueeze(-1)
+        has_traj = (traj == self.repertoire_tensor).any(-1)
+        has_traj = has_traj.view(tensordict.shape)
+        return tensordict[has_traj]
+
+
+def main():
+    # Create a CartPole environment with trajectory counting
+    env = GymEnv("CartPole-v1").append_transform(TrajCounter())
+
+    # Create a replay buffer with the completed trajectory repertoire transform
+    buffer = ReplayBuffer(
+        storage=LazyTensorStorage(1_000_000), transform=CompletedTrajectoryRepertoire()
+    )
+
+    # Roll out the environment for 1000 steps
+    while True:
+        rollout = env.rollout(1000, break_when_any_done=False)
+        if not rollout["next", "done"][-1].item():
+            break
+
+    # Extend the replay buffer with the rollout
+    buffer.extend(rollout)
+
+    # Get the last trajectory count
+    last_traj_count = rollout[-1]["next", "traj_count"].item()
+    print(f"Incomplete trajectory: {last_traj_count}")
+
+    # Sample from the replay buffer 10 times
+    for _ in range(10):
+        sample_traj_counts = buffer.sample(32)["next", "traj_count"].unique()
+        print(f"Sampled trajectories: {sample_traj_counts}")
+        assert last_traj_count not in sample_traj_counts
+
+
+if __name__ == "__main__":
+    main()