diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py
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+++ b/rl_coach/memories/non_episodic/distributed_experience_replay.py
@@ -0,0 +1,199 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Tuple, Union, Dict, Any
+
+import numpy as np
+import redis
+import uuid
+import pickle
+
+from rl_coach.core_types import Transition
+from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
+from rl_coach.utils import ReaderWriterLock
+
+
+class DistributedExperienceReplayParameters(MemoryParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 1000000)
+        self.allow_duplicates_in_batch_sampling = True
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.non_episodic.distributed_experience_replay:DistributedExperienceReplay'
+
+
+class DistributedExperienceReplay(Memory):
+    """
+    A regular replay buffer which stores transition without any additional structure
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True, 
+                 redis_ip = 'localhost', redis_port = 6379, db = 0):
+        """
+        :param max_size: the maximum number of transitions or episodes to hold in the memory
+        :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch
+        """
+        
+        super().__init__(max_size)
+        if max_size[0] != MemoryGranularity.Transitions:
+            raise ValueError("Experience replay size can only be configured in terms of transitions")
+        # self.transitions = []
+        self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling
+
+        self.db = db
+        self.redis_connection = redis.Redis(redis_ip, redis_port, self.db)
+
+    def length(self) -> int:
+        """
+        Get the number of transitions in the ER
+        """
+        return self.num_transitions()
+
+    def num_transitions(self) -> int:
+        """
+        Get the number of transitions in the ER
+        """
+        # Replace with distributed store len
+        return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys']
+        # return len(self.transitions)
+
+    def sample(self, size: int) -> List[Transition]:
+        """
+        Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
+        of samples available in the replay buffer then the batch will return empty.
+        :param size: the size of the batch to sample
+        :param beta: the beta parameter used for importance sampling
+        :return: a batch (list) of selected transitions from the replay buffer
+        """        
+        transition_idx = dict()
+        if self.allow_duplicates_in_batch_sampling:
+            while len(transition_idx) != size:
+                key = self.redis_connection.randomkey()
+                transition_idx[key] = pickle.loads(self.redis_connection.get(key))
+            # transition_idx = np.random.randint(self.num_transitions(), size=size)
+
+        else:
+            if self.num_transitions() >= size:
+                while len(transition_idx) != size:
+                    key = self.redis_connection.randomkey()
+                    if key in transition_idx:
+                        continue
+                    transition_idx[key] = pickle.loads(self.redis_connection.get(key))
+                # transition_idx = np.random.choice(self.num_transitions(), size=size, replace=False)
+            else:
+                raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. "
+                                 "There are currently {} transitions".format(self.num_transitions()))
+
+        # Replace with distributed store
+        batch = transition_idx.values()
+
+        return batch
+
+    def _enforce_max_length(self) -> None:
+        """
+        Make sure that the size of the replay buffer does not pass the maximum size allowed.
+        If it passes the max size, the oldest transition in the replay buffer will be removed.
+        This function does not use locks since it is only called internally
+        :return: None
+        """
+        granularity, size = self.max_size
+        if granularity == MemoryGranularity.Transitions:
+            while size != 0 and self.num_transitions() > size:
+                self.redis_connection.delete(self.redis_connection.randomkey())
+        else:
+            raise ValueError("The granularity of the replay buffer can only be set in terms of transitions")
+
+    def store(self, transition: Transition, lock: bool=True) -> None:
+        """
+        Store a new transition in the memory.
+        :param transition: a transition to store
+        :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class
+                     locks and then calls store with lock = True
+        :return: None
+        """
+        # Replace with distributed store
+
+        self.redis_connection.set(uuid.uuid4(), pickle.dumps(transition))
+        # self.transitions.append(transition)
+        self._enforce_max_length()
+
+    def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
+        """
+        Returns the transition in the given index. If the transition does not exist, returns None instead.
+        :param transition_index: the index of the transition to return
+        :param lock: use write locking if this is a shared memory
+        :return: the corresponding transition
+        """
+        # Replace with distributed store
+        import pytest; pytest.set_trace()
+        return pickle.loads(self.redis_connection.get(transition_index))
+        
+    def remove_transition(self, transition_index: int, lock: bool=True) -> None:
+        """
+        Remove the transition in the given index.
+
+        This does not remove the transition from the segment trees! it is just used to remove the transition
+        from the transitions list
+        :param transition_index: the index of the transition to remove
+        :return: None
+        """
+        # Replace with distributed store
+        import pytest; pytest.set_trace()
+        self.redis_connection.delete(transition_index)
+        
+    # for API compatibility
+    def get(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
+        """
+        Returns the transition in the given index. If the transition does not exist, returns None instead.
+        :param transition_index: the index of the transition to return
+        :return: the corresponding transition
+        """
+        # Replace with distributed store
+        import pytest; pytest.set_trace()
+        return self.get_transition(transition_index, lock)
+
+    # for API compatibility
+    def remove(self, transition_index: int, lock: bool=True):
+        """
+        Remove the transition in the given index
+        :param transition_index: the index of the transition to remove
+        :return: None
+        """
+        # Replace with distributed store
+        import pytest; pytest.set_trace()
+        self.remove_transition(transition_index, lock)
+
+    def clean(self, lock: bool=True) -> None:
+        """
+        Clean the memory by removing all the episodes
+        :return: None
+        """
+        import pytest; pytest.set_trace()
+        self.redis_connection.flushall()
+        # self.transitions = []
+
+    def mean_reward(self) -> np.ndarray:
+        """
+        Get the mean reward in the replay buffer
+        :return: the mean reward
+        """
+        # Replace with distributed store
+        import pytest; pytest.set_trace()
+        mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()])
+        # mean = np.mean([transition.reward for transition in self.transitions])
+
+        return mean