diff --git a/week5_explore/week5.ipynb b/week5_explore/week5.ipynb
index 4f9beb699..4bb327724 100644
--- a/week5_explore/week5.ipynb
+++ b/week5_explore/week5.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from abc import ABCMeta, abstractmethod, abstractproperty\n",
@@ -61,7 +63,9 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class BernoulliBandit:\n",
@@ -95,7 +99,9 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class AbstractAgent(metaclass=ABCMeta):   \n",
@@ -160,7 +166,9 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class EpsilonGreedyAgent(AbstractAgent):\n",
@@ -202,7 +210,9 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class UCBAgent(AbstractAgent):\n",
@@ -240,7 +250,9 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class ThompsonSamplingAgent(AbstractAgent):\n",
@@ -251,7 +263,9 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def plot_regret(env, agents, n_steps=5000, n_trials=50):\n",
@@ -339,7 +353,9 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class DriftingBandit(BernoulliBandit):\n",
@@ -431,7 +447,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# YOUR AGENT HERE SECTION"
@@ -519,7 +537,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import theano, theano.tensor as T\n",
@@ -544,7 +564,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class BNNAgent:\n",
@@ -650,7 +672,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "N_ITERS = 100"
@@ -659,7 +683,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def get_new_samples(states, action_rewards, batch_size=10):\n",
@@ -671,7 +697,9 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from IPython.display import clear_output\n",
@@ -695,7 +723,7 @@
     "            clear_output(True)\n",
     "            print(\"iteration #%i\\tmean reward=%.3f\\tmse=%.3f\\tkl=%.3f\"%(i,np.mean(rewards_history[-10:]),mse,kl))\n",
     "            plt.plot(rewards_history)\n",
-    "            plt.plot(pandas.ewma(np.array(rewards_history),alpha=0.1))\n",
+    "            plt.plot(moving_average(np.array(rewards_history),alpha=0.1))\n",
     "            plt.title(\"Reward per epesode\")\n",
     "            plt.xlabel(\"Episode\")\n",
     "            plt.ylabel(\"Reward\")\n",
@@ -710,7 +738,7 @@
     "            plt.title(\"p(Q(s, a))\")\n",
     "            plt.show()\n",
     "            \n",
-    "    return pandas.ewma(np.array(rewards_history),alpha=0.1)"
+    "    return moving_average(np.array(rewards_history),alpha=0.1)"
    ]
   },
   {
@@ -778,7 +806,9 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class ThompsonBNNAgent(BNNAgent):\n",
@@ -847,7 +877,9 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class BayesUCBBNNAgent(BNNAgent):\n",
@@ -976,7 +1008,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class RiverSwimEnv:\n",
@@ -1059,7 +1093,9 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class QLearningAgent:\n",
@@ -1094,7 +1130,9 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def train_mdp_agent(agent, env, n_episodes):\n",
@@ -1148,7 +1186,7 @@
     "rews = train_mdp_agent(agent, env, 1000)\n",
     "plt.figure(figsize=(15, 8))\n",
     "\n",
-    "plt.plot(pandas.ewma(np.array(rews),alpha=.1))\n",
+    "plt.plot(moving_average(np.array(rews),alpha=.1))\n",
     "plt.xlabel(\"Episode count\")\n",
     "plt.ylabel(\"Reward\")\n",
     "plt.show()"
@@ -1164,7 +1202,9 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def plot_policy(agent):\n",
@@ -1246,7 +1286,9 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def sample_normal_gamma(mu, lmbd, alpha, beta):\n",
@@ -1333,13 +1375,16 @@
     }
    ],
    "source": [
+    "from pandas import DataFrame\n",
+    "moving_average = lambda x, **kw: DataFrame({'x':np.asarray(x)}).x.ewm(**kw).mean().values\n",
+    "\n",
     "horizon = 20\n",
     "env = RiverSwimEnv(max_steps=horizon)\n",
     "agent = PsrlAgent(env.n_states, env.n_actions, horizon=horizon)\n",
     "rews = train_mdp_agent(agent, env, 1000)\n",
     "\n",
     "plt.figure(figsize=(15, 8))\n",
-    "plt.plot(pandas.ewma(np.array(rews), alpha=0.1))\n",
+    "plt.plot(moving_average(np.array(rews), alpha=0.1))\n",
     "\n",
     "plt.xlabel(\"Episode count\")\n",
     "plt.ylabel(\"Reward\")\n",
@@ -1404,7 +1449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,
diff --git a/week6_policy_based/homework_tensorflow.ipynb b/week6_policy_based/homework_tensorflow.ipynb
index 775e02aca..763a4725f 100644
--- a/week6_policy_based/homework_tensorflow.ipynb
+++ b/week6_policy_based/homework_tensorflow.ipynb
@@ -468,7 +468,9 @@
    "source": [
     "from IPython.display import clear_output\n",
     "from tqdm import trange\n",
-    "from pandas import ewma\n",
+    "from pandas import DataFrame\n",
+    "moving_average = lambda x, **kw: DataFrame({'x':np.asarray(x)}).x.ewm(**kw).mean().values\n",
+    "\n",
     "env_batch = EnvBatch(10)\n",
     "batch_states = env_batch.reset()\n",
     "\n",
@@ -511,12 +513,12 @@
     "        plt.figure(figsize=[8,4])\n",
     "        plt.subplot(1,2,1)\n",
     "        plt.plot(rewards_history, label='rewards')\n",
-    "        plt.plot(ewma(np.array(rewards_history),span=10), marker='.', label='rewards ewma@10')\n",
+    "        plt.plot(moving_aewmaverage(np.array(rewards_history),span=10), marker='.', label='rewards ewma@10')\n",
     "        plt.title(\"Session rewards\"); plt.grid(); plt.legend()\n",
     "        \n",
     "        plt.subplot(1,2,2)\n",
     "        plt.plot(entropy_history, label='entropy')\n",
-    "        plt.plot(ewma(np.array(entropy_history),span=1000), label='entropy ewma@1000')\n",
+    "        plt.plot(moving_average(np.array(entropy_history),span=1000), label='entropy ewma@1000')\n",
     "        plt.title(\"Policy entropy\"); plt.grid(); plt.legend()        \n",
     "        plt.show()\n",
     "        \n",