diff --git a/README.md b/README.md
index 9b812a21aa0..64559f7af37 100644
--- a/README.md
+++ b/README.md
@@ -99,68 +99,69 @@ lines of code*!
   
   from torchrl.collectors import SyncDataCollector
   from torchrl.data.replay_buffers import TensorDictReplayBuffer, \
-      LazyTensorStorage, SamplerWithoutReplacement
+    LazyTensorStorage, SamplerWithoutReplacement
   from torchrl.envs.libs.gym import GymEnv
   from torchrl.modules import ProbabilisticActor, ValueOperator, TanhNormal
   from torchrl.objectives import ClipPPOLoss
   from torchrl.objectives.value import GAE
   
-  env = GymEnv("Pendulum-v1")
+  env = GymEnv("Pendulum-v1") 
   model = TensorDictModule(
-      nn.Sequential(
-          nn.Linear(3, 128), nn.Tanh(),
-          nn.Linear(128, 128), nn.Tanh(),
-          nn.Linear(128, 128), nn.Tanh(),
-          nn.Linear(128, 2),
-          NormalParamExtractor()
-      ),
-      in_keys=["observation"],
-      out_keys=["loc", "scale"]
+    nn.Sequential(
+        nn.Linear(3, 128), nn.Tanh(),
+        nn.Linear(128, 128), nn.Tanh(),
+        nn.Linear(128, 128), nn.Tanh(),
+        nn.Linear(128, 2),
+        NormalParamExtractor()
+    ),
+    in_keys=["observation"],
+    out_keys=["loc", "scale"]
   )
   critic = ValueOperator(
-      nn.Sequential(
-          nn.Linear(3, 128), nn.Tanh(),
-          nn.Linear(128, 128), nn.Tanh(),
-          nn.Linear(128, 128), nn.Tanh(),
-          nn.Linear(128, 1),
-      ),
-      in_keys=["observation"],
+    nn.Sequential(
+        nn.Linear(3, 128), nn.Tanh(),
+        nn.Linear(128, 128), nn.Tanh(),
+        nn.Linear(128, 128), nn.Tanh(),
+        nn.Linear(128, 1),
+    ),
+    in_keys=["observation"],
   )
   actor = ProbabilisticActor(
-      model,
-      in_keys=["loc", "scale"],
-      distribution_class=TanhNormal,
-      distribution_kwargs={"min": -1.0, "max": 1.0},
-      return_log_prob=True
-      )
+    model,
+    in_keys=["loc", "scale"],
+    distribution_class=TanhNormal,
+    distribution_kwargs={"low": -1.0, "high": 1.0},
+    return_log_prob=True
+    )
   buffer = TensorDictReplayBuffer(
-      LazyTensorStorage(1000),
-      SamplerWithoutReplacement()
-      )
+    storage=LazyTensorStorage(1000),
+    sampler=SamplerWithoutReplacement(),
+    batch_size=50,
+    )
   collector = SyncDataCollector(
-      env,
-      actor,
-      frames_per_batch=1000,
-      total_frames=1_000_000
-      )
-  loss_fn = ClipPPOLoss(actor, critic, gamma=0.99)
+    env,
+    actor,
+    frames_per_batch=1000,
+    total_frames=1_000_000,
+  )
+  loss_fn = ClipPPOLoss(actor, critic)
+  adv_fn = GAE(value_network=critic, average_gae=True, gamma=0.99, lmbda=0.95)
   optim = torch.optim.Adam(loss_fn.parameters(), lr=2e-4)
-  adv_fn = GAE(value_network=critic, gamma=0.99, lmbda=0.95, average_gae=True)
+  
   for data in collector:  # collect data
-      for epoch in range(10):
-          adv_fn(data)  # compute advantage
-          buffer.extend(data.view(-1))
-          for i in range(20):  # consume data
-              sample = buffer.sample(50)  # mini-batch
-              loss_vals = loss_fn(sample)
-              loss_val = sum(
-                  value for key, value in loss_vals.items() if
-                  key.startswith("loss")
-                  )
-              loss_val.backward()
-              optim.step()
-              optim.zero_grad()
-      print(f"avg reward: {data['next', 'reward'].mean().item(): 4.4f}")
+    for epoch in range(10):
+        adv_fn(data)  # compute advantage
+        buffer.extend(data)
+        for sample in buffer:  # consume data
+            loss_vals = loss_fn(sample)
+            loss_val = sum(
+                value for key, value in loss_vals.items() if
+                key.startswith("loss")
+                )
+            loss_val.backward()
+            optim.step()
+            optim.zero_grad()
+    print(f"avg reward: {data['next', 'reward'].mean().item(): 4.4f}")
   ```
   </details>