@@ -54,7 +54,7 @@

    "metadata": {},

    "outputs": [],

    "source": [

-    "import functools\n",

+    "from functools import partial\n",

     "from random import random\n",

     "\n",

     "offset = random() - 0.5\n",

@@ -150,7 +150,7 @@

     "learner2 = adaptive.learner.Learner1D(f, bounds=(-1.01, 1.0))\n",

     "\n",

     "xs = np.linspace(-1.0, 1.0, len(learner.data))\n",

-    "learner2.add_data(xs, map(functools.partial(f, wait=False), xs))\n",

+    "learner2.add_data(xs, map(partial(f, wait=False), xs))\n",

     "\n",

     "learner2.plot()"

    ]

@@ -196,11 +196,50 @@

    "metadata": {},

    "outputs": [],

    "source": [

-    "learner = adaptive.AverageLearner(g, None, 0.03)\n",

+    "learner = adaptive.AverageLearner(g, None, 0.01)\n",

     "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 1)\n",

     "adaptive.live_plot(runner)"

    ]

   },

+  {

+   "cell_type": "markdown",

+   "metadata": {},

+   "source": [

+    "# Balancing learner"

+   ]

+  },

+  {

+   "cell_type": "markdown",

+   "metadata": {},

+   "source": [

+    "The balancing learner is a \"meta-learner\" that takes a list of multiple leaners. The runner wil find find out which points of which child learner will improve the loss the most and send those to the executor.\n",

+    "\n",

+    "The balancing learner can for example be used to implement a poor-man's 2D learner by using the `Learner1D`."

+   ]

+  },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {},

+   "outputs": [],

+   "source": [

+    "from adaptive.learner import Learner1D, BalancingLearner\n",

+    "\n",

+    "learners = [Learner1D(partial(f, offset=2*random()-1, wait=False), bounds=(-1.0, 1.0)) for i in range(10)]\n",

+    "learner = BalancingLearner(learners)\n",

+    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.02)"

+   ]

+  },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {},

+   "outputs": [],

+   "source": [

+    "import holoviews as hv\n",

+    "adaptive.live_plot(runner, plotter=lambda learner: hv.Overlay([L.plot() for L in learner.learners]))"

+   ]

+  },

   {

    "cell_type": "markdown",

    "metadata": {