diff --git a/acme/agents/tf/mcts/acting.py b/acme/agents/tf/mcts/acting.py
index 887d7c25e5..3a8673efbf 100644
--- a/acme/agents/tf/mcts/acting.py
+++ b/acme/agents/tf/mcts/acting.py
@@ -22,6 +22,7 @@
 from acme.agents.tf.mcts import models
 from acme.agents.tf.mcts import search
 from acme.agents.tf.mcts import types
+from acme.tf import utils as tf2_utils
 from acme.tf import variable_utils as tf2_variable_utils
 
 import dm_env
@@ -66,7 +67,9 @@ def __init__(
   def _forward(
       self, observation: types.Observation) -> Tuple[types.Probs, types.Value]:
     """Performs a forward pass of the policy-value network."""
-    logits, value = self._network(tf.expand_dims(observation, axis=0))
+    # Use tree.map_structure to support nested observation structures.
+    batched_observation = tf2_utils.add_batch_dim(observation)
+    logits, value = self._network(batched_observation)
 
     # Convert to numpy & take softmax.
     logits = logits.numpy().squeeze(axis=0)
diff --git a/acme/agents/tf/mcts/types.py b/acme/agents/tf/mcts/types.py
index 93d8d8cd11..3dcaf7479c 100644
--- a/acme/agents/tf/mcts/types.py
+++ b/acme/agents/tf/mcts/types.py
@@ -14,7 +14,7 @@
 
 """Type aliases and assumptions that are specific to the MCTS agent."""
 
-from typing import Callable, Tuple, Union
+from typing import Any, Callable, Tuple, Union
 import numpy as np
 
 # pylint: disable=invalid-name
@@ -22,8 +22,9 @@
 # Assumption: actions are scalar and discrete (integral).
 Action = Union[int, np.int32, np.int64]
 
-# Assumption: observations are array-like.
-Observation = np.ndarray
+# Observations can be array-like or nested structures (e.g., dicts, tuples).
+# This allows MCTS to work with environments that have complex observation spaces.
+Observation = Any
 
 # Assumption: rewards and discounts are scalar.
 Reward = Union[float, np.float32, np.float64]