diff --git a/rllib/agents/ppo/ppo_tf_policy.py b/rllib/agents/ppo/ppo_tf_policy.py
index f1a0089acd80..b0377524f9d7 100644
--- a/rllib/agents/ppo/ppo_tf_policy.py
+++ b/rllib/agents/ppo/ppo_tf_policy.py
@@ -113,27 +113,22 @@ def reduce_mean_valid(t):
 
     # Compute a value function loss.
     if policy.config["use_critic"]:
-        prev_value_fn_out = train_batch[SampleBatch.VF_PREDS]
-        vf_loss1 = tf.math.square(
+        vf_loss = tf.math.square(
             value_fn_out - train_batch[Postprocessing.VALUE_TARGETS]
         )
-        vf_clipped = prev_value_fn_out + tf.clip_by_value(
-            value_fn_out - prev_value_fn_out,
-            -policy.config["vf_clip_param"],
+        vf_loss_clipped = tf.clip_by_value(
+            vf_loss,
+            0,
             policy.config["vf_clip_param"],
         )
-        vf_loss2 = tf.math.square(
-            vf_clipped - train_batch[Postprocessing.VALUE_TARGETS]
-        )
-        vf_loss = tf.maximum(vf_loss1, vf_loss2)
-        mean_vf_loss = reduce_mean_valid(vf_loss)
+        mean_vf_loss = reduce_mean_valid(vf_loss_clipped)
     # Ignore the value function.
     else:
-        vf_loss = mean_vf_loss = tf.constant(0.0)
+        vf_loss_clipped = mean_vf_loss = tf.constant(0.0)
 
     total_loss = reduce_mean_valid(
         -surrogate_loss
-        + policy.config["vf_loss_coeff"] * vf_loss
+        + policy.config["vf_loss_coeff"] * vf_loss_clipped
         - policy.entropy_coeff * curr_entropy
     )
     # Add mean_kl_loss (already processed through `reduce_mean_valid`),
diff --git a/rllib/agents/ppo/ppo_torch_policy.py b/rllib/agents/ppo/ppo_torch_policy.py
index c45a7dcacf48..285ec95901c5 100644
--- a/rllib/agents/ppo/ppo_torch_policy.py
+++ b/rllib/agents/ppo/ppo_torch_policy.py
@@ -144,28 +144,19 @@ def reduce_mean_valid(t):
 
         # Compute a value function loss.
         if self.config["use_critic"]:
-            prev_value_fn_out = train_batch[SampleBatch.VF_PREDS]
             value_fn_out = model.value_function()
-            vf_loss1 = torch.pow(
+            vf_loss = torch.pow(
                 value_fn_out - train_batch[Postprocessing.VALUE_TARGETS], 2.0
             )
-            vf_clipped = prev_value_fn_out + torch.clamp(
-                value_fn_out - prev_value_fn_out,
-                -self.config["vf_clip_param"],
-                self.config["vf_clip_param"],
-            )
-            vf_loss2 = torch.pow(
-                vf_clipped - train_batch[Postprocessing.VALUE_TARGETS], 2.0
-            )
-            vf_loss = torch.max(vf_loss1, vf_loss2)
-            mean_vf_loss = reduce_mean_valid(vf_loss)
+            vf_loss_clipped = torch.clamp(vf_loss, 0, self.config["vf_clip_param"])
+            mean_vf_loss = reduce_mean_valid(vf_loss_clipped)
         # Ignore the value function.
         else:
-            vf_loss = mean_vf_loss = 0.0
+            vf_loss_clipped = mean_vf_loss = 0.0
 
         total_loss = reduce_mean_valid(
             -surrogate_loss
-            + self.config["vf_loss_coeff"] * vf_loss
+            + self.config["vf_loss_coeff"] * vf_loss_clipped
             - self.entropy_coeff * curr_entropy
         )