diff --git a/demo/README.md b/demo/README.md
new file mode 100644
index 0000000..15bfb67
--- /dev/null
+++ b/demo/README.md
@@ -0,0 +1,17 @@
+Ray Live Demo
+=============
+
+This demo can be run live while presenting to show Ray's capabilities.
+
+## Instructions
+1. Launch a Ray cluster on AWS with `ray up cluster_config.yaml`
+    - To run locally, install the requirements with `pip install -r requirements.txt`
+2. Connect to the head node
+    - I recommend using SSH with port forwarding in order to use Jupyter, Ray Dashboard, and Tensorboard without compromising security
+    - For example, `ssh -L 9999:127.0.0.1:8889 -L 9998:127.0.0.1:8080 -L 9997:127.0.0.1:6006 ubuntu@12.123.123.123` should map Jupyter to `127.0.0.1:9999`, Ray Dashboard to `127.0.0.1:9998` and Tensorboard to `127.0.0.1:9997`
+2. Open the jupyter notebooks on the cluster and set the `CLUSTER_ADDRESS` parameter in `ray_api_demo.ipynb` and `rllib_demo.ipynb`
+3. Also set links for Ray Dashboard and Tensorboard
+4. Run the live-coding presentation with [rise](https://rise.readthedocs.io/)
+    - Start a presentation with `Alt-r` or by pressing the button in the top right of the toolbar
+    - Use `SpaceBar` to navigate to the next slide and `Shift-SpaceBar` to navigate to the previous slide
+    - Use `Shift-Enter` to run the code in a cell
diff --git a/demo/cluster_config.yaml b/demo/cluster_config.yaml
new file mode 100644
index 0000000..6d85f5b
--- /dev/null
+++ b/demo/cluster_config.yaml
@@ -0,0 +1,114 @@
+# An unique identifier for the head node and workers of this cluster.
+cluster_name: ray-demo
+
+# The minimum number of workers nodes to launch in addition to the head
+# node. This number should be >= 0.
+min_workers: 2
+
+# The maximum number of workers nodes to launch in addition to the head
+# node. This takes precedence over min_workers.
+max_workers: 5
+
+# The initial number of worker nodes to launch in addition to the head
+# node. When the cluster is first brought up (or when it is refreshed with a
+# subsequent `ray up`) this number of nodes will be started.
+initial_workers: 2
+
+# Whether or not to autoscale aggressively. If this is enabled, if at any point
+#   we would start more workers, we start at least enough to bring us to
+#   initial_workers.
+autoscaling_mode: default
+
+# The autoscaler will scale up the cluster to this target fraction of resource
+# usage. For example, if a cluster of 10 nodes is 100% busy and
+# target_utilization is 0.8, it would resize the cluster to 13. This fraction
+# can be decreased to increase the aggressiveness of upscaling.
+# This value must be less than 1.0 for scaling to happen.
+target_utilization_fraction: 0.8
+
+# If a node is idle for this many minutes, it will be removed.
+idle_timeout_minutes: 5
+
+# Cloud-provider specific configuration.
+provider:
+    type: aws
+    region: us-west-2
+    # Availability zone(s), comma-separated, that nodes may be launched in.
+    # Nodes are currently spread between zones by a round-robin approach,
+    # however this implementation detail should not be relied upon.
+    availability_zone: us-west-2a,us-west-2b
+
+# How Ray will authenticate with newly launched nodes.
+auth:
+    ssh_user: ubuntu
+# By default Ray creates a new private keypair, but you can also use your own.
+# If you do so, make sure to also set "KeyName" in the head and worker node
+# configurations below.
+#    ssh_private_key: /path/to/your/key.pem
+
+# Provider-specific config for the head node, e.g. instance type. By default
+# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
+# For more documentation on available fields, see:
+# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+head_node:
+    InstanceType: m5.large
+    ImageId: ami-02c253ecf7eaba73e  # Deep Learning AMI (Ubuntu 16.04) Version 24.2
+
+    # You can provision additional disk space with a conf as follows
+    BlockDeviceMappings:
+        - DeviceName: /dev/sda1
+          Ebs:
+              VolumeSize: 100
+
+    # Additional options in the boto docs.
+
+# Provider-specific config for worker nodes, e.g. instance type. By default
+# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
+# For more documentation on available fields, see:
+# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+worker_nodes:
+    InstanceType: m5.large
+    ImageId: ami-0b294f219d14e6a82 # Deep Learning AMI (Ubuntu) Version 21.0
+
+    # Run workers on spot by default. Comment this out to use on-demand.
+    InstanceMarketOptions:
+        MarketType: spot
+        # Additional options can be found in the boto docs, e.g.
+        #   SpotOptions:
+        #       MaxPrice: MAX_HOURLY_PRICE
+
+    # Additional options in the boto docs.
+
+# Files or directories to copy to the head and worker nodes. The format is a
+# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
+file_mounts: {
+    "/home/ubuntu/ray_api_demo.ipynb": "./ray_api_demo.ipynb",
+    "/home/ubuntu/rllib_demo.ipynb": "./rllib_demo.ipynb",
+    "/home/ubuntu/utils.py": "./utils.py",
+}
+
+# List of shell commands to run to set up nodes.
+setup_commands:
+    - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
+    - pip install -U ray ray[rllib] ray[tune] ray[debug]
+    - pip install aiohttp psutil gym opencv-python
+    - pip install lz4
+
+# Custom commands that will be run on the head node after common setup.
+head_setup_commands:
+    - pip install RISE
+    - pip install boto3==1.4.8  # 1.4.8 adds InstanceMarketOptions
+    - pip install -U tensorflow
+
+# Custom commands that will be run on worker nodes after common setup.
+worker_setup_commands: []
+
+# Command to start ray on the head node. You don't need to change this.
+head_start_ray_commands:
+    - ray stop
+    - ulimit -n 65536; ray start --head --redis-port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --include-webui
+
+# Command to start ray on worker nodes. You don't need to change this.
+worker_start_ray_commands:
+    - ray stop
+    - ulimit -n 65536; ray start --redis-address=$RAY_HEAD_IP:6379 --object-manager-port=8076
diff --git a/demo/ray_api_demo.ipynb b/demo/ray_api_demo.ipynb
new file mode 100644
index 0000000..2d4430d
--- /dev/null
+++ b/demo/ray_api_demo.ipynb
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Ray API Demo: Parallel Training\n",
+    "\n",
+    "Demo created by [Peter Schafhalter](https://github.com/pschafhalter/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Training function\n",
+    "\n",
+    "Trains a classifier with a hyperparameter.\n",
+    "\n",
+    "Technical details:\n",
+    "- The classifier is a multi-layer perceptron.\n",
+    "- The hyperamater (alpha) is the regularization parameter.\n",
+    "- The value of alpha affects the [bias-variance tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff), which impacts whether the model underfits or overfits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import the scikit-learn machine learning library\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "\n",
+    "def train(alpha, train_x, test_x, train_y, test_y):\n",
+    "    # Instantiate a model with the given value of alpha\n",
+    "    classifier = MLPClassifier(alpha=alpha, max_iter=10000)\n",
+    "    # Train the model on the training data\n",
+    "    classifier.fit(train_x, train_y)\n",
+    "    # Evaluate the model on the test data and return the model's accuracy score\n",
+    "    predicted_y = classifier.predict(test_x)\n",
+    "    return accuracy_score(test_y, predicted_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Training with Hyperparameters\n",
+    "\n",
+    "- Try different values for alpha to train best model.\n",
+    "- Without parallelizing this is slow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Imports for profiling and visualization\n",
+    "from IPython.core.display import HTML\n",
+    "import time\n",
+    "import tqdm\n",
+    "\n",
+    "from utils import ray_get_with_progress_bar\n",
+    "\n",
+    "# Imports for generating the dataset\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import make_moons\n",
+    "\n",
+    "# Generate the dataset\n",
+    "x, y = make_moons(noise=0.8, random_state=0)\n",
+    "\n",
+    "# Set hyperparameters\n",
+    "trials = [10**-x for x in range(16)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train with different values for alpha\n",
+    "results = []\n",
+    "start = time.time()\n",
+    "for alpha in tqdm.tqdm(trials):\n",
+    "    results.append(train(alpha, *train_test_split(x, y)))\n",
+    "serial_time = time.time() - start\n",
+    "\n",
+    "# Print results\n",
+    "HTML(f\"<h3>Best accuracy: {max(results):.2f}</h3><h3>Total time: {serial_time:.2f} seconds</h3>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Parallel Training with Ray\n",
+    "\n",
+    "1. Import and set up ray with `ray.init()`\n",
+    "2. Add the `ray.remote` decorator to `train`\n",
+    "3. Replace calls to `train(...)` with `train.remote(...)`\n",
+    "4. Get the resulting python objects with `results = ray.get(results)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import ray\n",
+    "ray.shutdown()\n",
+    "ray.init()\n",
+    "\n",
+    "HTML(f\"\"\"<h3>Started Ray locally with:<h3>\n",
+    "<h3>{ray.cluster_resources()[\"CPU\"] : .0f} CPUs</h3>\n",
+    "<h3>{ray.cluster_resources()[\"memory\"]} GB of memory available</h3>\n",
+    "<h3>{ray.cluster_resources()[\"object_store_memory\"]} GB of object store memory</h3>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@ray.remote\n",
+    "def train(alpha, train_x, test_x, train_y, test_y):\n",
+    "    classifier = MLPClassifier(alpha=alpha, max_iter=10000)\n",
+    "    classifier.fit(train_x, train_y)\n",
+    "    predicted_y = classifier.predict(test_x)\n",
+    "    return accuracy_score(test_y, predicted_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train with different values for alpha\n",
+    "results = []\n",
+    "start = time.time()\n",
+    "for alpha in trials:\n",
+    "    # Call train with train.remote(...)\n",
+    "    results.append(train.remote(alpha, *train_test_split(x, y)))\n",
+    "\n",
+    "# Get results\n",
+    "# results = ray.get(results)   # This works just like the result below, but without progress bar\n",
+    "results = ray_get_with_progress_bar(results)\n",
+    "\n",
+    "parallel_time = time.time() - start\n",
+    "\n",
+    "# Print results\n",
+    "HTML(f\"\"\"<h3>Best accuracy: {max(results):.2f}</h3>\n",
+    "<h3>Total time: {parallel_time:.2f} seconds ({serial_time / parallel_time : .2f}x faster)</h3>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Parallel Training with Ray on a Cluster\n",
+    "\n",
+    "1. Launch a Ray cluster on AWS with `ray up cluster_config.yaml`\n",
+    "2. SSH into head node\n",
+    "3. Replace `ray.init()` with `ray.init(redis_address=\"...\")`\n",
+    "\n",
+    "The Ray Autoscalar can add and remove nodes as the workload changes. Currently, it integrates with AWS, GCP, Kubernetes, and private clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Connect to cluster\n",
+    "CLUSTER_ADDRESS = None # Set this to run on cluster\n",
+    "ray.shutdown()\n",
+    "ray.init(redis_address=CLUSTER_ADDRESS, include_webui=True)\n",
+    "\n",
+    "url = ray.get_webui_url()  # Override this in case of SSH forwarding from the cluster\n",
+    "HTML(f\"\"\"<h3>Connected to Ray cluster with:<h3>\n",
+    "<h3>{ray.cluster_resources()[\"CPU\"] : .0f} CPUs</h3>\n",
+    "<h3>{ray.cluster_resources()[\"memory\"]} GB of memory available</h3>\n",
+    "<h3>{ray.cluster_resources()[\"object_store_memory\"]} GB of object store memory</h3>\n",
+    "<br>\n",
+    "<a href='{url}'>Dashboard</a>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train with different values for alpha\n",
+    "results = []\n",
+    "start = time.time()\n",
+    "for alpha in trials:\n",
+    "    # Call train with train.remote(...)\n",
+    "    results.append(train.remote(alpha, *train_test_split(x, y)))\n",
+    "\n",
+    "# Get results\n",
+    "# results = ray.get(results)   # This works just like the result below, but without progress bar\n",
+    "results = ray_get_with_progress_bar(results)\n",
+    "\n",
+    "cluster_time = time.time() - start\n",
+    "\n",
+    "# Print results\n",
+    "HTML(f\"\"\"<h3>Best accuracy: {max(results):.2f}</h3>\n",
+    "<h3>Total time: {cluster_time:.2f} seconds ({serial_time / parallel_time : .2f}x faster)</h3>\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train with different values for alpha\n",
+    "cluster_trials = [i for i in range(100)]\n",
+    "results = []\n",
+    "start = time.time()\n",
+    "for alpha in cluster_trials:\n",
+    "    # Call train with train.remote(...)\n",
+    "    results.append(train.remote(alpha, *train_test_split(x, y)))\n",
+    "\n",
+    "# Get results\n",
+    "# results = ray.get(results)   # This works just like the result below, but without progress bar\n",
+    "results = ray_get_with_progress_bar(results)\n",
+    "\n",
+    "cluster_large_workload_time = time.time() - start\n",
+    "\n",
+    "# Print results\n",
+    "HTML(f\"\"\"<h3>Best accuracy: {max(results):.2f}</h3>\n",
+    "<h3>Total time: {cluster_large_workload_time:.2f} seconds ({cluster_large_workload_time / serial_time : .2f}x slower)</h3>\n",
+    "<h3>Total trials: {len(cluster_trials)}\\t({round(len(cluster_trials) / len(trials))}x more trials)\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "HTML(f\"\"\"<h3>Time without Ray:\\t{serial_time:.2f} seconds</h3>\n",
+    "<h3>Time with Ray:\\t{parallel_time:.2f} seconds ({serial_time / parallel_time :.2f}x faster)</h3>\n",
+    "<h3>Time with Ray on Cluster:\\t{cluster_time:.2f} seconds ({serial_time / cluster_time :.2f}x faster)</h3>\n",
+    "<h3>Ran {round(len(cluster_trials) / len(trials))}x more trials on cluster in {cluster_large_workload_time:.2f} seconds ({cluster_large_workload_time / serial_time : .2f}x slower)\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Note: speedup doesn't exactly scale with cores due to overhead.\n",
+    "\n",
+    "Speedup tends to become linear as the number of tasks increases, or tasks become longer."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/requirements.txt b/demo/requirements.txt
new file mode 100644
index 0000000..b496529
--- /dev/null
+++ b/demo/requirements.txt
@@ -0,0 +1,102 @@
+absl-py==0.8.1
+aiohttp==3.6.2
+astor==0.8.0
+async-timeout==3.0.1
+atomicwrites==1.3.0
+attrs==19.2.0
+backcall==0.1.0
+bleach==3.1.0
+boto3==1.9.248
+botocore==1.12.248
+certifi==2019.9.11
+chardet==3.0.4
+Click==7.0
+cloudpickle==1.2.2
+colorama==0.4.1
+decorator==4.4.0
+defusedxml==0.6.0
+docutils==0.15.2
+entrypoints==0.3
+filelock==3.0.12
+funcsigs==1.0.2
+future==0.18.0
+gast==0.3.2
+google-pasta==0.1.7
+grpcio==1.24.1
+gym==0.15.3
+h5py==2.10.0
+idna==2.8
+idna-ssl==1.1.0
+importlib-metadata==0.23
+ipykernel==5.1.2
+ipython==7.8.0
+ipython-genutils==0.2.0
+jedi==0.15.1
+Jinja2==2.10.3
+jmespath==0.9.4
+joblib==0.14.0
+jsonschema==3.1.1
+jupyter-client==5.3.4
+jupyter-core==4.6.0
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+lz4==2.2.1
+Markdown==3.1.1
+MarkupSafe==1.1.1
+mistune==0.8.4
+more-itertools==7.2.0
+multidict==4.5.2
+nbconvert==5.6.0
+nbformat==4.4.0
+notebook==6.0.1
+numpy==1.17.2
+opencv-python==4.1.1.26
+packaging==19.2
+pandocfilters==1.4.2
+parso==0.5.1
+pexpect==4.7.0
+pickleshare==0.7.5
+pluggy==0.13.0
+prometheus-client==0.7.1
+prompt-toolkit==2.0.10
+protobuf==3.10.0
+psutil==5.6.3
+ptyprocess==0.6.0
+py==1.8.0
+py-spy==0.2.2
+pyglet==1.3.2
+Pygments==2.4.2
+pyparsing==2.4.2
+pyrsistent==0.15.4
+pytest==5.2.1
+python-dateutil==2.8.0
+PyYAML==5.1.2
+pyzmq==18.1.0
+ray==0.7.5
+redis==3.3.10
+requests==2.22.0
+rise==5.5.1
+s3transfer==0.2.1
+scikit-learn==0.21.3
+scipy==1.3.1
+Send2Trash==1.5.0
+setproctitle==1.1.10
+six==1.12.0
+sklearn==0.0
+tensorboard==1.14.0
+tensorflow==1.14.0
+tensorflow-estimator==1.14.0
+termcolor==1.1.0
+terminado==0.8.2
+testpath==0.4.2
+tornado==6.0.3
+tqdm==4.36.1
+traitlets==4.3.3
+typing-extensions==3.7.4
+urllib3==1.25.6
+wcwidth==0.1.7
+webencodings==0.5.1
+Werkzeug==0.16.0
+wrapt==1.11.2
+yarl==1.3.0
+zipp==0.6.0
diff --git a/demo/rllib_demo.ipynb b/demo/rllib_demo.ipynb
new file mode 100644
index 0000000..8ae4c4a
--- /dev/null
+++ b/demo/rllib_demo.ipynb
@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# RLlib Demo: Solving Mountain Car\n",
+    "\n",
+    "<img src=\"http://skyai.org/wiki/?plugin=ref&page=Documentation%2FTutorial%20-%20Example%20-%20Mountain%20Car&src=mountaincar.png\" width=\"800px\" />\n",
+    "\n",
+    "Demo created by [Peter Schafhalter](https://github.com/pschafhalter/)\n",
+    "\n",
+    "Image source: [skyai.org](http://skyai.org/wiki/?plugin=ref&page=Documentation%2FTutorial%20-%20Example%20-%20Mountain%20Car&src=mountaincar.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import ray\n",
+    "from ray import tune\n",
+    "from utils import MOUNTAINCAR_DEFAULT_CONFIG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "CLUSTER_ADDRESS = None  # Set this value to run on cluster\n",
+    "ray.init(redis_address=CLUSTER_ADDRESS, include_webui=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Configuring RLlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "```json\n",
+    "config = {\n",
+    "   \"env\": \"MountainCarContinuous-v0\",\n",
+    "   \"actor_hiddens\": [32, 64],\n",
+    "   \"critic_hiddens\": [64, 64],\n",
+    "   \"actor_lr\": 1e-3,\n",
+    "   \"critic_lr\": 1e-3,\n",
+    "   \"l2_reg\": 1e-5,\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "config = MOUNTAINCAR_DEFAULT_CONFIG.copy()\n",
+    "config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Running an Experiment\n",
+    "\n",
+    "<!-- Set this link to open tensorflow while presenting -->\n",
+    "[Tensorboard](http://127.0.0.1:6006)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Modify the l2 regularization hyperparameter\n",
+    "config[\"l2_reg\"] = 1e-1\n",
+    "\n",
+    "# Run the RL experiment using the DDPG algorithm\n",
+    "tune.run(\"DDPG\", stop={\n",
+    "      \"time_total_s\": 60  # Stop after 60 seconds\n",
+    "    },\n",
+    "    config=config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Searching for Hyperparameters\n",
+    "\n",
+    "1. Replace hyperparameter's value with `tune.grid_search([1, 2, 3, 4, 5])`\n",
+    "   - `l2_reg=1e-1` becomes `l2_reg=tune.grid_search([1e-1, 1e-3, 1e-5, 1e-7])`\n",
+    "   - Can also sample from a continuous PDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Run a grid search in parallel for the l2 regularization hyperparameter\n",
+    "config[\"l2_reg\"] = tune.grid_search([1e-1, 1e-3, 1e-5, 1e-7])\n",
+    "\n",
+    "tune.run(\"DDPG\", stop={\n",
+    "      \"episode_reward_mean\": 90,  # Stop if the algorithm achieves a mean reward of 90\n",
+    "      \"time_total_s\": 600         # Stop after 600 seconds\n",
+    "    },\n",
+    "    config=config)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/utils.py b/demo/utils.py
new file mode 100644
index 0000000..2339d41
--- /dev/null
+++ b/demo/utils.py
@@ -0,0 +1,54 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tqdm
+import ray
+
+MOUNTAINCAR_DEFAULT_CONFIG = {
+    "env": "MountainCarContinuous-v0",
+    "actor_hiddens": [32, 64],
+    "critic_hiddens": [64, 64],
+    "n_step": 3,
+    "model": {},
+    "gamma": 0.99,
+    "env_config": {},
+    "exploration_should_anneal": True,
+    "schedule_max_timesteps": 100000,
+    "timesteps_per_iteration": 1000,
+    "exploration_fraction": 0.4,
+    "exploration_final_scale": 0.02,
+    "exploration_ou_noise_scale": 0.75,
+    "exploration_ou_theta": 0.15,
+    "exploration_ou_sigma": 0.2,
+    "target_network_update_freq": 0,
+    "tau": 0.01,
+    "buffer_size": 50000,
+    "prioritized_replay": False,
+    "prioritized_replay_alpha": 0.6,
+    "prioritized_replay_beta": 0.4,
+    "prioritized_replay_eps": 1.0e-06,
+    "clip_rewards": False,
+    "actor_lr": 0.001,
+    "critic_lr": 0.001,
+    "use_huber": False,
+    "huber_threshold": 1.0,
+    "l2_reg": 1.0e-05,
+    "learning_starts": 1000,
+    "sample_batch_size": 1,
+    "train_batch_size": 64,
+    "num_workers": 0,
+    "num_gpus_per_worker": 0,
+    "per_worker_exploration": False,
+    "worker_side_prioritization": False,
+    "evaluation_interval": 5,
+    "evaluation_num_episodes": 10
+}
+
+
+def ray_get_with_progress_bar(object_ids):
+    ready = []
+    remaining = object_ids
+    for _ in tqdm.tqdm(range(len(object_ids))):
+        ready, remaining = ray.wait(remaining)
+    return ray.get(ready)