Work around TF Agents issue: env.step(1) => env.step(np.array(1))

18_reinforcement_learning.ipynb

@@ -1860,13 +1860,20 @@
     "env.reset()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning**: since TF Agents 0.4.0, there seems to be an issue with passing an integer to the `env.step()` method (it raises an `AttributeError`). You need to wrap it in a NumPy array, as done below. Please see [TF Agents Issue #520](https://github.com/tensorflow/agents/issues/520) for more details."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
-    "env.step(1) # Fire"
+    "env.step(np.array(1)) # Fire"
    ]
   },
   {
@@ -2074,9 +2081,9 @@
    "source": [
     "env.seed(42)\n",
     "env.reset()\n",
-    "time_step = env.step(1) # FIRE\n",
+    "time_step = env.step(np.array(1)) # FIRE\n",
     "for _ in range(4):\n",
-    "    time_step = env.step(3) # LEFT"
+    "    time_step = env.step(np.array(3)) # LEFT"
    ]
   },
   {
@@ -2215,7 +2222,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create the replay buffer:"
+    "Create the replay buffer (this may use a lot of RAM, so please reduce the buffer size if you get an out-of-memory error):"
    ]
   },
   {
@@ -2521,7 +2528,7 @@
     "    lives = tf_env.pyenv.envs[0].ale.lives()\n",
     "    if prev_lives != lives:\n",
     "        tf_env.reset()\n",
-    "        tf_env.pyenv.envs[0].step(1)\n",
+    "        tf_env.pyenv.envs[0].step(np.array(1))\n",
     "        prev_lives = lives\n",
     "\n",
     "watch_driver = DynamicStepDriver(\n",
@@ -2790,7 +2797,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.8"
   }
  },
  "nbformat": 4,