diff --git a/rllib/agents/ars/ars.py b/rllib/agents/ars/ars.py
index ef74f1a3e..ef2195213 100644
--- a/rllib/agents/ars/ars.py
+++ b/rllib/agents/ars/ars.py
@@ -47,6 +47,10 @@ DEFAULT_CONFIG = with_common_config({
         "num_envs_per_worker": 1,
         "observation_filter": "NoFilter"
     },
+
+    # Use the new "trajectory view API" to collect samples and produce
+    # model- and policy inputs.
+    "_use_trajectory_view_api": True,
 })
 # __sphinx_doc_end__
 # yapf: enable
@@ -327,7 +331,7 @@ class ARSTrainer(Trainer):
 
     @override(Trainer)
     def compute_action(self, observation, *args, **kwargs):
-        action, _, _ = self.policy.compute_actions(observation, update=True)
+        action, _, _ = self.policy.compute_actions([observation], update=True)
         if kwargs.get("full_fetch"):
             return action[0], [], {}
         return action[0]
diff --git a/rllib/agents/ars/ars_tf_policy.py b/rllib/agents/ars/ars_tf_policy.py
index b482449d8..9adcc1561 100644
--- a/rllib/agents/ars/ars_tf_policy.py
+++ b/rllib/agents/ars/ars_tf_policy.py
@@ -68,9 +68,9 @@ class ARSTFPolicy(Policy):
                         add_noise=False,
                         update=True,
                         **kwargs):
-        # Batch is given as list of one.
-        if isinstance(observation, list) and len(observation) == 1:
-            observation = observation[0]
+        # Squeeze batch dimension (we always calculate actions for only a
+        # single obs).
+        observation = observation[0]
         observation = self.preprocessor.transform(observation)
         observation = self.observation_filter(observation[None], update=update)
 
diff --git a/rllib/agents/es/es.py b/rllib/agents/es/es.py
index d669bd5d0..065f141f5 100644
--- a/rllib/agents/es/es.py
+++ b/rllib/agents/es/es.py
@@ -45,6 +45,10 @@ DEFAULT_CONFIG = with_common_config({
         "num_envs_per_worker": 1,
         "observation_filter": "NoFilter"
     },
+
+    # Use the new "trajectory view API" to collect samples and produce
+    # model- and policy inputs.
+    "_use_trajectory_view_api": True,
 })
 # __sphinx_doc_end__
 # yapf: enable
@@ -324,7 +328,7 @@ class ESTrainer(Trainer):
 
     @override(Trainer)
     def compute_action(self, observation, *args, **kwargs):
-        action, _, _ = self.policy.compute_actions(observation, update=False)
+        action, _, _ = self.policy.compute_actions([observation], update=False)
         if kwargs.get("full_fetch"):
             return action[0], [], {}
         return action[0]
diff --git a/rllib/agents/es/es_tf_policy.py b/rllib/agents/es/es_tf_policy.py
index ad19b7ba5..6c2bf206b 100644
--- a/rllib/agents/es/es_tf_policy.py
+++ b/rllib/agents/es/es_tf_policy.py
@@ -47,7 +47,7 @@ def rollout(policy, env, timestep_limit=None, add_noise=False, offset=0.0):
     observation = env.reset()
     for _ in range(timestep_limit or max_timestep_limit):
         ac, _, _ = policy.compute_actions(
-            observation, add_noise=add_noise, update=True)
+            [observation], add_noise=add_noise, update=True)
         ac = ac[0]
         observation, r, done, _ = env.step(ac)
         if offset != 0.0:
@@ -118,9 +118,9 @@ class ESTFPolicy(Policy):
                         add_noise=False,
                         update=True,
                         **kwargs):
-        # Batch is given as list of one.
-        if isinstance(observation, list) and len(observation) == 1:
-            observation = observation[0]
+        # Squeeze batch dimension (we always calculate actions for only a
+        # single obs).
+        observation = observation[0]
         observation = self.preprocessor.transform(observation)
         observation = self.observation_filter(observation[None], update=update)
         # `actions` is a list of (component) batches.
diff --git a/rllib/agents/es/tests/test_es.py b/rllib/agents/es/tests/test_es.py
index c29761193..94d8b0f74 100644
--- a/rllib/agents/es/tests/test_es.py
+++ b/rllib/agents/es/tests/test_es.py
@@ -18,7 +18,7 @@ class TestES(unittest.TestCase):
         config["num_workers"] = 1
         config["episodes_per_batch"] = 10
         config["train_batch_size"] = 100
-        # Test eval workers ("normal" Trainer eval WorkerSet, unusual for ARS).
+        # Test eval workers ("normal" Trainer eval WorkerSet).
         config["evaluation_interval"] = 1
         config["evaluation_num_workers"] = 2