diff --git a/rllib/agents/maml/maml_torch_policy.py b/rllib/agents/maml/maml_torch_policy.py
index 2e0e1e208..695826798 100644
--- a/rllib/agents/maml/maml_torch_policy.py
+++ b/rllib/agents/maml/maml_torch_policy.py
@@ -8,8 +8,8 @@ from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.agents.ppo.ppo_tf_policy import setup_config
 from ray.rllib.agents.ppo.ppo_torch_policy import vf_preds_fetches, \
     ValueNetworkMixin
-from ray.rllib.utils.framework import try_import_torch
 from ray.rllib.utils.torch_ops import apply_grad_clipping
+from ray.rllib.utils.framework import try_import_torch
 
 torch, nn = try_import_torch()
 
@@ -178,7 +178,7 @@ class MAMLLoss(object):
 
                 # Meta Update
                 ppo_loss, s_loss, kl_loss, v_loss, ent = self.compute_losses(
-                    fnet, self.inner_adaptation_steps, i, clip_loss=True)
+                    fnet, self.inner_adaptation_steps - 1, i, clip_loss=True)
 
                 inner_loss = torch.mean(
                     torch.stack([
@@ -271,8 +271,14 @@ def maml_loss(policy, model, dist_class, train_batch):
 
         # `split` may not exist yet (during test-loss call), use a dummy value.
         # Cannot use get here due to train_batch being a TrackingDict.
-        split = train_batch["split"] if "split" in train_batch else \
-            torch.tensor([[8, 8], [8, 8]])
+        if "split" in train_batch:
+            split = train_batch["split"]
+        else:
+            split_shape = (policy.config["inner_adaptation_steps"],
+                           policy.config["num_workers"])
+            split_const = int(train_batch["obs"].shape[0] //
+                              (split_shape[0] * split_shape[1]))
+            split = torch.ones(split_shape, dtype=int) * split_const
         policy.loss_obj = MAMLLoss(
             model=model,
             dist_class=dist_class,