diff --git a/mctx/_src/policies.py b/mctx/_src/policies.py
index f170db9..f47fec7 100644
--- a/mctx/_src/policies.py
+++ b/mctx/_src/policies.py
@@ -115,6 +115,7 @@ def muzero_policy(
   action_weights = summary.visit_probs
   action_logits = _apply_temperature(
       _get_logits_from_probs(action_weights), temperature)
+  action_logits = _mask_invalid_actions(action_logits, invalid_actions)
   action = jax.random.categorical(rng_key, action_logits)
   return base.PolicyOutput(
       action=action,
@@ -368,6 +369,7 @@ def stochastic_muzero_policy(
   action_weights = summary.visit_probs
   action_logits = _apply_temperature(
       _get_logits_from_probs(action_weights), temperature)
+  action_logits = _mask_invalid_actions(action_logits, invalid_actions)
   action = jax.random.categorical(rng_key, action_logits)
   return base.PolicyOutput(
       action=action, action_weights=action_weights, search_tree=search_tree)
diff --git a/mctx/_src/tests/policies_test.py b/mctx/_src/tests/policies_test.py
index 549dca9..f6b1d97 100644
--- a/mctx/_src/tests/policies_test.py
+++ b/mctx/_src/tests/policies_test.py
@@ -16,6 +16,7 @@
 import functools
 
 from absl.testing import absltest
+from absl.testing import parameterized
 import jax
 import jax.numpy as jnp
 import mctx
@@ -359,5 +360,56 @@ def test_stochastic_muzero_policy(self):
                                policy_output.action_weights)
 
 
+class PolicyActionMaskTest(parameterized.TestCase):
+
+  @parameterized.product(
+      stochastic=[False, True],
+      use_jit=[False, True],
+      temperature=[0.0, 1.0, 100.0],
+  )
+  def test_sampled_actions_respect_mask(self, stochastic, use_jit, temperature):
+    batch_size, num_actions = 32, 4
+    valid_actions = jnp.arange(batch_size) % num_actions
+    invalid_actions = jnp.arange(num_actions)[None, :] != valid_actions[:, None]
+    root = mctx.RootFnOutput(
+        prior_logits=jnp.zeros((batch_size, num_actions)),
+        value=jnp.zeros(batch_size),
+        embedding=jnp.zeros((batch_size, 1)),
+    )
+    rewards = jnp.zeros_like(root.prior_logits)
+    if stochastic:
+      decision_fn, chance_fn = _make_bandit_decision_and_chance_fns(rewards, 2)
+      policy = functools.partial(
+          mctx.stochastic_muzero_policy,
+          decision_recurrent_fn=decision_fn,
+          chance_recurrent_fn=chance_fn,
+      )
+    else:
+      policy = functools.partial(
+          mctx.muzero_policy,
+          recurrent_fn=_make_bandit_recurrent_fn(
+              rewards, dummy_embedding=jnp.zeros_like(root.embedding)
+          ),
+      )
+    policy = functools.partial(
+        policy,
+        params=(),
+        root=root,
+        num_simulations=2,
+        invalid_actions=invalid_actions,
+        dirichlet_fraction=0.0,
+        temperature=temperature,
+    )
+    if use_jit:
+      policy = jax.jit(policy)
+
+    output = policy(rng_key=jax.random.PRNGKey(0))
+
+    np.testing.assert_array_equal(output.action, valid_actions)
+    np.testing.assert_allclose(
+        output.action_weights, jax.nn.one_hot(valid_actions, num_actions)
+    )
+
+
 if __name__ == "__main__":
   absltest.main()