diff --git a/lol.py b/lol.py
deleted file mode 100644
index 22f461d8071..00000000000
--- a/lol.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import torch
-import tqdm
-from tensordict.nn import TensorDictModule, TensorDictSequential
-from torchrl.envs.libs.gym import GymEnv
-from torchrl.modules import ProbabilisticActor, LSTMModule, MLP
-from torchrl.collectors import SyncDataCollector
-from torchrl.objectives import DiscreteSACLoss
-from torchrl.envs import (
-    ParallelEnv,
-    TransformedEnv,
-    InitTracker,
-    StepCounter,
-    RewardSum,
-)
-
-
-def create_model(input_size, output_size, hidden_size=256, num_layers=3, out_key="logits"):
-
-    lstm_module = LSTMModule(
-        input_size=input_size,
-        hidden_size=hidden_size,
-        num_layers=num_layers,
-        in_key="observation",
-        out_key="features",
-    )
-    mlp = TensorDictModule(
-        MLP(
-            in_features=hidden_size,
-            out_features=output_size,
-            num_cells=[],
-        ),
-        in_keys=["features"],
-        out_keys=[out_key],
-    )
-
-    inference_model = TensorDictSequential(lstm_module, mlp)
-    training_model = TensorDictSequential(lstm_module.set_recurrent_mode(), mlp)
-
-    return inference_model, training_model
-
-
-def create_rhs_transform(input_size, hidden_size=256, num_layers=3):
-    lstm_module = LSTMModule(
-        input_size=input_size,
-        hidden_size=hidden_size,
-        num_layers=num_layers,
-        in_key="observation",
-        out_key="features",
-    )
-    return lstm_module.make_tensordict_primer()
-
-
-def main():
-
-    device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
-    test_env = GymEnv("CartPole-v1", device=device, categorical_action_encoding=True)
-    action_spec = test_env.action_spec.space
-    observation_spec = test_env.observation_spec["observation"]
-
-    def create_env_fn():
-        env = GymEnv("CartPole-v1", device=device, categorical_action_encoding=True)
-        env = TransformedEnv(env)
-        env.append_transform(create_rhs_transform(input_size=observation_spec.shape[-1]))
-        env.append_transform(InitTracker())
-        return env
-
-    # Models
-    ##################
-
-    inference_actor, training_actor = create_model(input_size=observation_spec.shape[-1], output_size=action_spec.n)
-    inference_actor = ProbabilisticActor(
-        module=inference_actor,
-        in_keys=["logits"],
-        out_keys=["action"],
-        distribution_class=torch.distributions.Categorical,
-        return_log_prob=True,
-    )
-    training_actor = ProbabilisticActor(
-        module=training_actor,
-        in_keys=["logits"],
-        out_keys=["action"],
-        distribution_class=torch.distributions.Categorical,
-        return_log_prob=True,
-    )
-    inference_actor = inference_actor.to(device)
-    training_actor = training_actor.to(device)
-    _, training_critic = create_model(input_size=observation_spec.shape[-1], output_size=action_spec.n)
-    training_critic = training_critic.to(device)
-
-    # Collector
-    ##################
-
-    collector = SyncDataCollector(
-        create_env_fn=create_env_fn,
-        policy=inference_actor,
-        frames_per_batch=100,
-        total_frames=1000,
-        device=device,
-        storing_device=device,
-        max_frames_per_traj=-1,
-        split_trajs=False,
-    )
-
-    # Loss
-    ##################
-
-    loss_module = DiscreteSACLoss(
-        actor_network=training_actor,
-        qvalue_network=training_critic,
-        num_actions=action_spec.n,
-        num_qvalue_nets=2,
-        loss_function="smooth_l1",
-    )
-
-    # Collection loop
-    ##################
-
-    for data in tqdm.tqdm(collector):
-
-        # *** TypeError: cannot assign 'torch.cuda.FloatTensor' as parameter 'weight_ih_l0'
-        # (torch.nn.Parameter or None expected)
-        data = loss_module(data)
-
-    collector.shutdown()
-
-
-if __name__ == "__main__":
-    main()