diff --git a/rllib/agents/qmix/qmix.py b/rllib/agents/qmix/qmix.py
index 264267f5b..8a6222605 100644
--- a/rllib/agents/qmix/qmix.py
+++ b/rllib/agents/qmix/qmix.py
@@ -1,10 +1,12 @@
 from ray.rllib.agents.trainer import with_common_config
 from ray.rllib.agents.dqn.dqn import GenericOffPolicyTrainer
 from ray.rllib.agents.qmix.qmix_policy import QMixTorchPolicy
-from ray.rllib.execution.replay_ops import MixInReplay
+from ray.rllib.execution.replay_ops import SimpleReplayBuffer, Replay, \
+    StoreToReplayBuffer
 from ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches
 from ray.rllib.execution.train_ops import TrainOneStep, UpdateTargetNetwork
 from ray.rllib.execution.metric_ops import StandardMetricsReporting
+from ray.rllib.execution.concurrency_ops import Concurrently
 from ray.rllib.optimizers import SyncBatchReplayOptimizer
 
 # yapf: disable
@@ -102,16 +104,22 @@ def make_sync_batch_optimizer(workers, config):
 # Experimental distributed execution impl; enable with "use_exec_api": True.
 def execution_plan(workers, config):
     rollouts = ParallelRollouts(workers, mode="bulk_sync")
+    replay_buffer = SimpleReplayBuffer(config["buffer_size"])
 
-    train_op = rollouts \
-        .for_each(MixInReplay(config["buffer_size"])) \
+    store_op = rollouts \
+        .for_each(StoreToReplayBuffer(local_buffer=replay_buffer))
+
+    train_op = Replay(local_buffer=replay_buffer) \
         .combine(
             ConcatBatches(min_batch_size=config["train_batch_size"])) \
         .for_each(TrainOneStep(workers)) \
         .for_each(UpdateTargetNetwork(
             workers, config["target_network_update_freq"]))
 
-    return StandardMetricsReporting(train_op, workers, config)
+    merged_op = Concurrently(
+        [store_op, train_op], mode="round_robin", output_indexes=[1])
+
+    return StandardMetricsReporting(merged_op, workers, config)
 
 
 QMixTrainer = GenericOffPolicyTrainer.with_updates(
diff --git a/rllib/execution/replay_ops.py b/rllib/execution/replay_ops.py
index fc2ec3993..516505dd1 100644
--- a/rllib/execution/replay_ops.py
+++ b/rllib/execution/replay_ops.py
@@ -93,6 +93,32 @@ def Replay(*,
     return LocalIterator(gen_replay, SharedMetrics())
 
 
+class SimpleReplayBuffer:
+    """Simple replay buffer that operates over batches."""
+
+    def __init__(self, num_slots, replay_proportion: float = None):
+        """Initialize SimpleReplayBuffer.
+
+        Args:
+            num_slots (int): Number of batches to store in total.
+        """
+        self.num_slots = num_slots
+        self.replay_batches = []
+        self.replay_index = 0
+
+    def add_batch(self, sample_batch):
+        if self.num_slots > 0:
+            if len(self.replay_batches) < self.num_slots:
+                self.replay_batches.append(sample_batch)
+            else:
+                self.replay_batches[self.replay_index] = sample_batch
+                self.replay_index += 1
+                self.replay_index %= self.num_slots
+
+    def replay(self):
+        return random.choice(self.replay_batches)
+
+
 class MixInReplay:
     """This operator adds replay to a stream of experiences.
 
@@ -102,63 +128,40 @@ class MixInReplay:
     number of replay slots.
     """
 
-    def __init__(self, num_slots, replay_proportion: float = None):
+    def __init__(self, num_slots, replay_proportion: float):
         """Initialize MixInReplay.
 
         Args:
             num_slots (int): Number of batches to store in total.
-            replay_proportion (float): If None, one batch will be replayed per
-                each input batch. Otherwise, the input batch will be returned
+            replay_proportion (float): The input batch will be returned
                 and an additional number of batches proportional to this value
                 will be added as well.
 
         Examples:
-            # 1:1 mode (default)
-            >>> replay_op = MixInReplay(rollouts, 100)
-            >>> print(next(replay_op))
-            SampleBatch(<replay>)
-
-            # proportional mode
+            # replay proportion 2:1
             >>> replay_op = MixInReplay(rollouts, 100, replay_proportion=2)
             >>> print(next(replay_op))
             [SampleBatch(<input>), SampleBatch(<replay>), SampleBatch(<rep.>)]
 
-            # proportional mode, replay disabled
+            # replay proportion 0:1, replay disabled
             >>> replay_op = MixInReplay(rollouts, 100, replay_proportion=0)
             >>> print(next(replay_op))
             [SampleBatch(<input>)]
         """
-        if replay_proportion is not None:
-            if replay_proportion > 0 and num_slots == 0:
-                raise ValueError(
-                    "You must set num_slots > 0 if replay_proportion > 0.")
-        elif num_slots == 0:
+        if replay_proportion > 0 and num_slots == 0:
             raise ValueError(
-                "You must set num_slots > 0 if replay_proportion = None.")
-        self.num_slots = num_slots
+                "You must set num_slots > 0 if replay_proportion > 0.")
+        self.replay_buffer = SimpleReplayBuffer(num_slots)
         self.replay_proportion = replay_proportion
-        self.replay_batches = []
-        self.replay_index = 0
 
     def __call__(self, sample_batch):
         # Put in replay buffer if enabled.
-        if self.num_slots > 0:
-            if len(self.replay_batches) < self.num_slots:
-                self.replay_batches.append(sample_batch)
-            else:
-                self.replay_batches[self.replay_index] = sample_batch
-                self.replay_index += 1
-                self.replay_index %= self.num_slots
+        self.buffer.add_batch(sample_batch)
 
-        # 1:1 replay mode.
-        if self.replay_proportion is None:
-            return random.choice(self.replay_batches)
-
-        # Proportional replay mode.
+        # Proportional replay.
         output_batches = [sample_batch]
         f = self.replay_proportion
         while random.random() < f:
             f -= 1
-            replay_batch = random.choice(self.replay_batches)
-            output_batches.append(replay_batch)
+            output_batches.append(self.replay_buffer.replay())
         return output_batches