diff --git a/ngclearn/components/neurons/graded/rewardErrorCell.py b/ngclearn/components/neurons/graded/rewardErrorCell.py
index 9e13f153f..b5a0877b8 100755
--- a/ngclearn/components/neurons/graded/rewardErrorCell.py
+++ b/ngclearn/components/neurons/graded/rewardErrorCell.py
@@ -9,8 +9,11 @@ class RewardErrorCell(JaxComponent): ## Reward prediction error cell
 
     | --- Cell Input Compartments: ---
     | reward - current reward signal at time `t`
+    | accum_reward - current accumulated episodic reward signal
     | --- Cell Output Compartments: ---
     | mu - current moving average prediction of reward at time `t`
+    | rpe - current reward prediction error (RPE) signal
+    | accum_reward - current accumulated episodic reward signal (IF online predictor not used)
 
     Args:
         name: the string name of this cell
@@ -18,12 +21,22 @@ class RewardErrorCell(JaxComponent): ## Reward prediction error cell
         n_units: number of cellular entities (neural population size)
 
         alpha: decay factor to apply to (exponential) moving average prediction
+
+        ema_window_len: exponential moving average window length -- for use only
+            in `evolve` step for updating episodic reward signals; (default: 10)
+
+        use_online_predictor: use online prediction of reward signal (default: True)
+            -- if set to False, then reward prediction will only occur upon a call
+            to this cell's `evolve` function
     """
-    def __init__(self, name, n_units, alpha, batch_size=1, **kwargs):
+    def __init__(self, name, n_units, alpha, ema_window_len=10,
+                 use_online_predictor=True, batch_size=1, **kwargs):
         super().__init__(name, **kwargs)
 
         ## RPE meta-parameters
         self.alpha = alpha
+        self.ema_window_len = ema_window_len
+        self.use_online_predictor = use_online_predictor
 
         ## Layer Size Setup
         self.n_units = n_units
@@ -34,29 +47,55 @@ def __init__(self, name, n_units, alpha, batch_size=1, **kwargs):
         self.mu = Compartment(restVals) ## reward predictor state(s)
         self.reward = Compartment(restVals) ## target reward signal(s)
         self.rpe = Compartment(restVals) ## reward prediction error(s)
+        self.accum_reward = Compartment(restVals)  ## accumulated reward signal(s)
+        self.n_ep_steps = Compartment(jnp.zeros((self.batch_size, 1))) ## number of episode steps taken
 
     @staticmethod
-    def _advance_state(dt, alpha, mu, rpe, reward):
+    def _advance_state(dt, use_online_predictor, alpha, mu, rpe, reward,
+                       n_ep_steps, accum_reward):
         ## compute/update RPE and predictor values
+        accum_reward = accum_reward + reward
         rpe = reward - mu
-        mu = mu * (1. - alpha) + reward * alpha
-        return mu, rpe
+        if use_online_predictor:
+            mu = mu * (1. - alpha) + reward * alpha
+        n_ep_steps = n_ep_steps + 1
+        return mu, rpe, n_ep_steps, accum_reward
 
     @resolver(_advance_state)
-    def advance_state(self, mu, rpe):
+    def advance_state(self, mu, rpe, n_ep_steps, accum_reward):
         self.mu.set(mu)
         self.rpe.set(rpe)
+        self.n_ep_steps.set(n_ep_steps)
+        self.accum_reward.set(accum_reward)
+
+    @staticmethod
+    def _evolve(dt, use_online_predictor, ema_window_len, n_ep_steps, mu,
+                accum_reward):
+        if use_online_predictor:
+            ## total episodic reward signal
+            r = accum_reward/n_ep_steps
+            mu = (1. - 1./ema_window_len) * mu + (1./ema_window_len) * r
+        return mu
+
+    @resolver(_evolve)
+    def evolve(self, mu):
+        self.mu.set(mu)
 
     @staticmethod
     def _reset(batch_size, n_units):
-        mu = jnp.zeros((batch_size, n_units)) #None
-        rpe = jnp.zeros((batch_size, n_units)) #None
-        return mu, rpe
+        restVals = jnp.zeros((batch_size, n_units))
+        mu = restVals
+        rpe = restVals
+        accum_reward = restVals
+        n_ep_steps = jnp.zeros((batch_size, 1))
+        return mu, rpe, accum_reward, n_ep_steps
 
     @resolver(_reset)
-    def reset(self, mu, rpe):
+    def reset(self, mu, rpe, accum_reward, n_ep_steps):
         self.mu.set(mu)
         self.rpe.set(rpe)
+        self.accum_reward.set(accum_reward)
+        self.n_ep_steps.set(n_ep_steps)
 
     @classmethod
     def help(cls): ## component help function
@@ -69,16 +108,23 @@ def help(cls): ## component help function
                 {"reward": "External reward signals/values"},
             "outputs":
                 {"mu": "Current state of reward predictor",
-                 "rpe": "Current value of reward prediction error at time `t`"},
+                 "rpe": "Current value of reward prediction error at time `t`",
+                 "accum_reward": "Current accumulated episodic reward signal (generally "
+                                 "produced at the end of a control episode of `n_steps`)",
+                 "n_ep_steps": "Number of episodic steps taken/tracked thus far "
+                               "(since last `reset` call)",
+                 "use_online_predictor": "Should an online reward predictor be used/maintained?"},
         }
         hyperparams = {
             "n_units": "Number of neuronal cells to model in this layer",
             "alpha": "Moving average decay factor",
+            "ema_window_len": "Exponential moving average window length",
             "batch_size": "Batch size dimension of this component"
         }
         info = {cls.__name__: properties,
                 "compartments": compartment_props,
-                "dynamics": "rpe = reward - mu; mu = mu * (1 - alpha) + reward * alpha",
+                "dynamics": "rpe = reward - mu; mu = mu * (1 - alpha) + reward * alpha; "
+                            "accum_reward = accum_reward + reward",
                 "hyperparameters": hyperparams}
         return info