{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Space Invasion, Space Invaders, and Climate Change Mitigation\n", "\n", "## [Scott Jeen](https://enjeeneer.io/)\n", "\n", "### Jesus College MCR, 3rd June 2021" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "**Abstract**\n", "\n", "Recent advances in the field of Reinforcement Learning, a subfield of Artificial Intelligence, have shown computers can achieve superhuman performance at complex games like Go, Starcraft and the Atari Suite without human knowledge. What if, instead of playing Space Invaders, we play the game of climate change mitigation? More specifically, how can we design games where the goal is to minimise carbon emissions in some setting, and learn to play them optimally? In this talk, we'll first establish the importance of using less energy and material to limit planet-cooking, then we'll take the first steps toward formulating a game whose solution could help to mitigate climate change." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 0. Prerequisites\n", "1. Agree that runaway climate change is worth avoiding\n", "2. Willing to accept approximations/back-of-the-envelope calcs\n", "3. Able to look beyond my UK-centric lens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 0. Outline\n", "**1. Space Invasion:** the footprint of climate change mitigation\n", "\n", "**2. Space Invaders:** learning to control complex systems optimally\n", "\n", "**3. An Emission-minimisation Game**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Every good university course begins with some prerequisites, and despite this talk being conducted in a dining room, today will be no different. I expect three things from you all this evening: 1) That you agree runaway climate change is something worth avoiding, I only say this because I will not be discussing why it's worth avoiding, and I'll assume we're all on the same page, thinking together about the best strategies for doing so. Number 2) I expect that you will willingly accept my sometimes poor back-of-the-envelope calculations; the first half of this talk will be about sustainable energy at a national, or international scale, and to make the presentation of that information more relatable the numbers are often rounded to one significant figure. And finally prerequisite number 3), this talk focusses mainly on the United Kingdom, despite the fact the problem is global, and despite the fact the UK's role in climate change mitigation is almost unimportant when compared with other larger countries. I hope that despite this, findings related to the UK can generalise to all countries, as we all have to face these challenges at some stage or other.\n", "\n", "If we're all happy with the prerequisites, let's begin with the first topic from the talk title: space invasion." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 1. Space Invasion" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Climate change mitigation is fundamentally an energy problem; society requires energy of all forms to function, and this energy still comes largely from sources that emit greenhouse gases that cause global warming. With that in mind, we can think of energy as the calories of our economies, and we can frame climate change mitigation as a diet that we must embark on. Like any diet, there are three things we can do to lose weight. Firstly, we can eat healthier meals, or get our energy from cleaner sources. We can think of this as analogous to generating energy from renewable energy resources like wind or solar. Secondly, we can eat less food, or use less energy in our lives. In our research group we call this Resource Efficiency, or maintaning the same final service for less energy input. And finally, we can keep eat the same rubbish food in similar amounts, and just exercise a few hours a day to try and burn off the calories. This is analgoous to carbon capture and storage, where we continue to emit carbon dioxide by burning fossil fuels, but attach a hoover to the exhaust of the power station or industrial process and bury the gases in the same oilfield you just plucked the fossil fuels from. Guess which option big oil like the best. \n", "\n", "So these are our options. The UK has focussed largely on option 1 (renewable energy) to-date, and all of their projections rely on option 3 (carbon capture), little attention is given to motivating resource efficiency; in much the same way people prefer not to tell you to eat less when dieting. I'm going to talk initially about the problems associated with the UK's plans, and how my research looks to address it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## High Carb(on) Diet" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## High Carb(on) Diet\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "To do that, I need to introduce you to our unit of measurement for this diet. Instead of using the colorie, we'll use the kilowatt-hour (kWh), the SI unit for measuring energy. To give you a sense of scale, here's our current energy diet, and the number of kWhs associated with some everyday activities. \n", "\n", "Leaving one 40W lightbulb on for 24 hours will use about 1kWh of energy. An aluminium can of coke requires about 0.6kWh of energy to be produced. A hot bath will cost you 5kWh. And burning 1 litre of petrol in your car by driving 5 or 6 miles will use 10kWh of energy. If we sum all activities you do daily, we find that the average person in the UK uses about 125kWh/day. Instead of quantifying energy use on a daily basis, we can convert this volume of energy to a flow rate of energy that we call power, measured in watts rather than watt-hours. So 125kWh/day of energy is equivalent to a 5kW power source being consuming power for the whole day, similar to two cookers being left at 200 degrees all day. \n", "\n", "So this is your life, two cookers on all the time, one for the left hemisphere of your brain and one for the right. So to continue to live your life as you do today, you need to go out in the world and find a 5kW power source and have it on all day, offsetting the energy you are using elsewhere in your life. So let's think about doing that; if you're going to go out and find a source of power, your going to need a piece of land to put in on. How much land would you like? Well let's give everyone in the country a fair share of land. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Our Energy Diet\n", "💡 → 1kWh\n", "\n", "🥤 → 0.6kWh\n", "\n", "🛁 → 5kWh\n", "\n", "🚗 → 10kWh\n", "\n", "💁‍♀️ → 125kWh/day or **5kW** → 🎛🎛 \n", "\n", "after **MacKay (2008)**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "The population of the UK is 66.65 million apparently, and the total area of land in the UK is 242,495 square kilometres. If we divide the top one by the bottom one, we find that each person in the UK gets about 3600 square metres of space to store their energy generating devices, which is about equivalent to half a football pitch. We can do one more calculation to find the power density of your life: by taking the power rating of your life (5kW) and dividing it by your allotment size (3600m2) we find that your life costs 2.5 W/m2.\n", "\n", "Now, this is a useful figure to obtain, because each of those energy generating devices you're about to go and source are also quantified in this way, so we see how likely it is for each of the technologies to power your life." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Your Allotment for Growing Energy\n", "- UK population: 66.65 million \n", "- UK land area: 242,495 km²\n", "- Your allotment: 3600m² ~ half a football pitch\n", "- Your life: **~2.5 W/m$^2$**\n", "\n", "\n", "\n", "after **MacKay (2008)**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Here's a table of the most common energy generating technologies. The middle column is their power density, or power output per unit land area, and the final column is an indication of whether they are zero-emission or otherwise. (The pinnochio face is reserved for technologies that are often said to be renewable, but are, in fact, not). So, remember you're life requires 2.5 W/m2, what the first entry in the table is telling us is that to power your life using solely wind power we need to cover your entire allotment (and everyone else's allotment) is wind turbines. If we wanted to use solar power, we'd need to cover about half of your football pitch in solar panels. If we wanted to use nuclear power, we would only need to use about 1% of your football pitch. And so on. So let's try and visualise some of these interventions." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Energy Space Requirements\n", "Your life: **~2.5 W/m$^2$**\n", "\n", "\n", "| Technology | ~ Power Per Unit Land Area | Zero Operational Emissions |\n", "|-------------|----------------------------|----------------------------|\n", "| Wind | 2.7 W/m$^2$ | 💚 |\n", "| Biomass | 1 W/m$^2$ | 🤥 |\n", "| Solar | 6 W/m$^2$ | 💚 |\n", "| Hydropower | 0.9 W/m$^2$ | 🤥 |\n", "| Geothermal | 3 W/m$^2$ | 💚 |\n", "| Natural Gas | 1300 W/m$^2$ | ⛔️ |\n", "| Coal | 125 W/m$^2$ | ⛔️ |\n", "| Oil | 180 W/m$^2$ | ⛔️ |\n", "| Nuclear | 300 W/m$^2$ | 💚 |\n", "\n", "Data: **van Zalk & Behrens (2018)**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Here's your football pitch:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "If you want to reserve some space for living your life, rather than just producing energy, you'll want to corden that off. Here's is 40% of your land blocked off, which represents the area of UK land currently used for cities and for countryside Bossard & Otahel (2000)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "But we'll find later on that we might need some of that land to avoid turning the UK into a desert, so it's back on the table for now:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Let's take a look at how this land has been used through the years. This was (very) roughly the UK between the beginning of the industrial revolution and the 1980s. Most electricity coming from coal, and oil used for transportation and some heating. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then Mrs Thatcher came to power in the 1980s, closed most of the coal mines and replaced them with natural gas power plants of higher power densithy and lower emission intensity, and the energy mix looked something like this. Note: energy generation is using approximately 1% of land area here, plenty of room to continue live out the glorious 80s whilst the planet slowly begins to simmer." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Here's (very) roughly where we are today, we have a mix of natural gas, biomass, wind, solar and nuclear; with the area of land they cover flattered by the fact that 40% of our energy is sourced from natural gas. The important takeaway from this diagram is the arbitrarily sized chequered box representing imported energy. Much of the goods and services that contribute to your 125kWh per day energy usage are imported; the aluminimum in your coke can, the steel in your car. To fund our lifestyles in the UK, we are effectively outsourcing a large amount of energy production (and associated emissions), moving the energy generating devices from our land to somebody else's land. This may be okay today, when the energy generating devices elsewhere are natural gas or coal and don't take up much land. But when these countries attempt to decarbonise and find they are being asked or required to build solar panels on their countryside, I suspect they are going to come back to the UK and say 'wait a minute, you deal with these emissions, you're causing them'. This gives us a false sense of how much land the energy required to fuel our lifestyles is taking up.\n", "\n", "So, to me, it seems only fair that we consider scenarios where we generate an amount of energy proportional to our lifestyles. And that is what we will consider now." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we were to generate energy using only wind and solar, we would use approximately 70% of UK land area, remember cities and countryside account for 40% of land area so we are eating into our leisure area now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are various ways to stop this space invasion, and one of them is to shrink the size of the energy pie; to use less energy in our society; to be more resource efficient. That is the focus of our research group's work, and that is one of the goals of my research. So, if we want to reduce our energy demand, what are our options, and where should our prioritises lie." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Reducing energy demand: what are our options?\n", "\n", "\n", "\n", "**Cullen & Allwood (2010)**\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "So if we need to reduce our energy demand, what are our options? Well, earlier I discussed the average energy demand of someone in the UK and the summation of a bunch of different tasks, jobs or activities we conduct throughout the day. We can think of these tasks as *services*, and it is the use of these services we can alter to reduce our energy demand. \n", "\n", "Above, is work done by my supervisor, Jon Cullen, in 2010, mapping the relationship between energy sources and final services, via the devices that produce this transformation. I'd like to first focus your attention on the middle slice of the five illustrated. You'll see that approximately half of global energy supply is used to produce or extract heat (red); the most obvious example of this being in the heating and cooling of homes, other examples include powering furnaces in manufacuturing facilities, or in heating water. Just over a third of our energy is used to produce motion (cyan), be it powering cars, trucks or planes, or in moving heavy machinery in manufacuturing. And just over 15% of energy remains for what Jon describes as 'Other' (black) which involves lighting, communication, and myriad of less energy-intensive services.\n", "\n", "So the big-ticket items for energy reduction are in changing how we move, and changing how we regulate temperature. In general, there's two change we can make: 1) Increase the efficiency of energy-converting devices such that they demand less energy for the same final service, or 2) Decrease energy-use altogether, either in the production or use phases of a final service.\n", "\n", "An example of the former, in the context or personal car use would be increasing the thermodynamic efficiency of the engine such that the car uses less fuel to cover the same distance. An example of the later would be driving the car for fewer miles, or using less steel in construction of the vehicle.\n", "\n", "My research looks to affect two parts of this diagram, I'll discuss both in turn.\n", "\n", "The first is the small cyan line in the bottom left hand corner: renewable energy, and its integration with energy conversion devices, and final services. Although small on this diagram, this was the global energy mix in 2005, by 2050 we expect renewable energy to supply approximately 70% of world energy. Unlike fossil fuel power stations, we cannot control when renewable energy resources will produce electricity because their ability to produce energy is weather dependent. For example, solar panels will produce energy during the daytime when the sun has risen, but not at night when the sun has set. We therefore say renewable energy technologies are *intermittent*. I'll explain the intermittency problem visually:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy import interpolate\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.stats as stats\n", "from scipy.interpolate import interp1d\n", "from matplotlib import animation, rc\n", "from IPython.display import HTML\n", "\n", "# time series\n", "x = np.linspace(0,24,25)\n", "x_new = np.linspace(0,24,250)\n", "\n", "# demand\n", "demand = [1,1,1,1,1,1,2,6,3,2,2,2,1,1,1,1,1,4,7,9,8,6,3,1,0]\n", "f = interp1d(x, demand, kind='cubic')\n", "demand_new = f(x_new)\n", "\n", "# grid\n", "grid_mu = 12\n", "grid_sig = 4\n", "grid = stats.norm.pdf(x_new, grid_mu, grid_sig) * 100\n", "\n", "# figure\n", "with plt.xkcd():\n", " fig = plt.figure(figsize=(15,6))\n", " ax = plt.subplot(1,1,1) \n", " ax.set_xlabel('time (hrs)')\n", " ax.set_ylabel('power')\n", " plt.yticks([])\n", " plt.xticks([0,6,12,18,24])\n", " ax.set_xlim((0,24))\n", " ax.set_ylim((0,12))\n", " line1, = ax.plot([], [], label='demand')\n", " line2, = ax.plot([], [], label='supply')\n", " plt.legend()\n", " \n" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "# animation function\n", "def drawframe(n=int):\n", " x1 = x_new[:n]\n", " y1 = demand_new[:n]\n", " y2 = grid[:n]\n", " \n", "\n", " line1.set_data(x1, y1)\n", " line2.set_data(x1, y2)\n", " \n", " return (line1, line2)\n", "\n", "# create animation\n", "rc('animation', html='html5')\n", "anim = animation.FuncAnimation(fig, drawframe, frames=len(x_new), interval=50)\n", "# anim.save('images/intermittency.gif')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Let's consider the problem of trying to power a flat on Park Street for one day using a 10kW array of solar panels on the roof. The orange line here traces power output of the panels throughout the day, when the sun rises in the morning it begins to produce energy, it's power output peaks at some point in the early afternoon and then drops to 0 after the sun has set. In contrast the energy demand of the flat occupants is lumpy, there's a peak around 7am when people get in the shower and put the kettle on for a coffee; if they go out to work for the day the power demand drops in the morning and afternoon, then when they come home and putting the heating, oven and tv in there's another spike. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## The Intermittency Problem\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create fill between plot\n", "with plt.xkcd():\n", " fig = plt.figure(figsize=(15,6))\n", " ax = plt.subplot(1,1,1) \n", " ax.set_xlabel('time (hrs)')\n", " ax.set_ylabel('power')\n", " plt.yticks([])\n", " plt.xticks([0,6,12,18,24])\n", " ax.set_xlim((0,24))\n", " ax.set_ylim((0,12))\n", " ax.plot(x_new, demand_new, label='demand')\n", " ax.plot(x_new, grid, label='supply')\n", " plt.fill_between(x_new, grid, demand_new, where=(demand_new>grid), color='red', alpha=0.2, label='deficit')\n", " plt.fill_between(x_new, grid, demand_new, where=(demand_new" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "demand_new_2 = stats.norm.pdf(x_new, grid_mu, 6) * 80\n", "\n", "# create fill between plot\n", "with plt.xkcd():\n", " fig = plt.figure(figsize=(15,6))\n", " ax = plt.subplot(1,1,1) \n", " ax.set_xlabel('time (hrs)')\n", " ax.set_ylabel('power')\n", " plt.yticks([])\n", " plt.xticks([0,6,12,18,24])\n", " ax.set_xlim((0,24))\n", " ax.set_ylim((0,12))\n", " ax.plot(x_new, demand_new_2, label='demand')\n", " ax.plot(x_new, grid, label='supply')\n", " plt.fill_between(x_new, grid, demand_new_2, where=(demand_new_2>grid), color='blue', alpha=0.1, label='discharged')\n", " plt.fill_between(x_new, grid, demand_new_2, where=(demand_new_2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "This is of course an idealised representation of what we'd like to do at both a household and national scale, and it makes it look simpler that it is. In reality, both the the supply curve and the demand curve are stochastic, and matching them is a highly complex optimisation problem.\n", "\n", "To do so we need to accurately predict renewable energy supply at various time horizons; in the next 5 minutes and in the next 5 hours, and everywhere in between. Equally we need to predict energy demand, which, depending on the situation can be affected by countless variables. In the household setting, the energy demand is largely affected by weather conditions (i.e. if it's cold outside heating demand increases) and time of day (if it's 7am somebody probably wants a shower). In the industrial setting, perhaps a car manufacturing plant, the energy demand of the plant may be affected by sales forecasts, and raw material delivery schedules.\n", "\n", "Attempting to solve this problem requires some sophisticated techniques, and my research centres on a subfield of artificial intelligence called Reinforcement Learning (RL). Which I'll take about now, in section 2: space invaders. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Intermittency: A non-trivial optimsation problem\n", "- **Stochastic supply:**\n", " - Weather-dependent\n", " - Need accurate predictions of supply at varied time horizons: 5mins; 5 hours; 5 days\n", " \n", "- **Stochastic demand:**\n", " - Depending on scenario, affected by countless variables\n", " - Household setting: outdoor temperature; time of day; day of week\n", " - Industrial setting: sales forecasts; raw material delivery schedules " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 2. Space Invaders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reinforcement learning is the eminent paradigm for allowing computers to learn from experience. In RL, we consider an agent (represented by the brain) interacting with an environment (represented by the globe). The agent can take actions in an environment, and at each timestep step it receives feedback on how the state of the environment has changed given that action, and a reward for taking that action. The goal of the RL agent is to maximise the sum of cumulative rewards throughout its lifetime. When an RL agent is instantiated, it has no understanding of its environment or valuable actions to take in the environment, but learns by taking actions in the environment, in a trial and error fashion, observing the reward it accrues, and updating it's understanding of the state-space. It's hypothesised that this is much the same way we as humans learn intelligently. Consider the example of learning to ride a bike; we don't necessarily understand the physics of bike-riding, instead we get on the bike, try and balance and cycle and see what works for us. We find, through trial and error, the optimal way to position our body to balance the bike.\n", "\n", "Although this appears a simple paradigm, it elicits extremelely powerful behaviour from computers." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Reinforcement Learning\n", "\n", "\n", "\n", "**Silver (2015)**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Some of the best examples of this powerful behaviour come from the gaming domain. Here's an RL agent, created by DeepMind in 2013, that learns to play the Atari game breakout with no prior knowledge or human input. The agent is fed minimal data; it's fed the raw pixels of the screen as a representation of the state; it's knows it can take three actions: move left, move right, or stay still, and it's fed the score at the top of the screen as a reward signal.\n", "\n", "You can see initially it struggles to hit the ball, but slowly it learns first to connect with the ball. Then by episode 600 it exhibits some pretty intelligent behaviour; it discovers that the optimally strategy is to burrow holes in the channels of the bricks and ping the ball behind the bricks." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "\n", "YouTubeVideo('TmPfTpjtdgg', height=400, width=800)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's another example, a few years later DeepMind created a Go-playing agent and played the world no.1 Lee Sedol in a 5 match series in South Korea. Go is considerably more complex game than chess; instead of an 8x8 board you have a 19x19 board, meaning the number of state permutations is astronimical. You can arrange the Go board in roughly $10^(170)$ permutations which is more atoms that are in the observable universe, meaning if you were to store each state permutation in a computer you would need one larger than the size of our universe.\n", "\n", "AlphaGo beat Lee Sedol 4-1 having trained on a few millions episodes of game play, but perhaps the most intersting aspect of their match was the now infamous move 37 played by AlphaGo. On top is the board arrangement at move 37, apparently the most humans believe that with this board set-up the optimal stragey is the play in the margins, and avoid moving too close to the centre. AlphaGo elected to play 5 columns in from the margin, which flummoxed Lee Sedol and the audience of spectators, so much so that he had to go outside for a cigarette.\n", "\n", "As the game evolved this move proved to be crucial. The board evolved in such that this black piece won AlphaGo the game, it from the near-infinite trajectories this game could have gone down, AlphaGo had correctly predicted the most likely. Not only did AlphaGo win the game, but it taught humans something about a complex system that we hadn't appreciated in the 3000 years we've been playing the game of Go, and to me that's really exciting." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## AlphaGo: Mastering the game of Go\n", "\n", "\n", "\n", "\n", "\n", "**Silver et al (2017)**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To give you a little more intuition about what is happening under the hood of an RL agent, let's return quickly to the breakout example.\n", "\n", "There are various ways in which the agent can represent its understanding of the system, but one is by assinging each part of the state-space (the environment) a value, and iterating it's formulation of that value as it receives rewards from the environment.\n", "\n", "Here we see a plot of the agent's value estimate for the current state of the environment over time. We see a spikey pattern in the value estimate; just before the ball hits it bricks it has learned that it will receive reward shortly and estimates high value, then the value estimate drops as it knows it would receive more reward until some time into the future." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "YouTubeVideo('DG17IKcDt8c', height=400, width=800)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 3. An Emission-Minimising Game" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So I've introduced RL, and discussed how agents can learn to play extremely complex games with no prior knowledge to a superhuman level. I've also motivated the need to reduce energy-use as a climate change mitigation technique. I'm going to conclude by combining these two sections together, and discuss formulating an emission-minimising game and how we can go about formulating a solution." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## 3.1 Formulating the Game\n", "- Moves:\n", " - Chiller control\n", " - $\\sum_{n=1}^6 a_t^n \\in \\mathcal{A}$, with $\\mathcal{A} = [0\\text{kW}, 5\\text{kW}]$\n", "- Goal:\n", " - Minimise energy-use\n", " - $\\min{E_t}$, whilst\n", " - Maintain temperature\n", " - $T_t \\leq 3^0C$\n", "- Environment Dynamics:\n", " - ?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Learning the Game Dynamics\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## 3.2 Playing the Game\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## 3.3. Winning the Game\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## 3.4 Next Steps\n", "- Incorportating grid randomness\n", "- Incorporating uncertainty\n", "- Online, data-efficient learning\n", "- Multi-agent systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Reading\n", "**Further explanations on why these techniques work**\n", "- [Can neural networks solve any problem?](https://towardsdatascience.com/can-neural-networks-really-learn-any-function-65e106617fc6)\n", "- [Visualising how LSTMs make predictions](https://distill.pub/2019/memorization-in-rnns/)\n", "\n", "**Introductory Books**\n", "- [The art of statistics: how to learn from data - David Spiegelhalter (2018)](https://www.amazon.co.uk/Art-Statistics-Learning-Pelican-Books/dp/0241398630)\n", "- [Life 3.0: Being Human in the Age of Artificial Intelligence - Max Tegmark (2017)](https://www.amazon.co.uk/dp/B07474JB3Q/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Thanks!\n", "- Notes: https://enjeeneer.io/talks/2021-03-15-xchangeseminar/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## References \n", "Bossard, M., Feranec, J., & Otahel, J. (2000). CORINE land cover technical guide: Addendum 2000.\n", "\n", "Cullen, J. M., & Allwood, J. M. (2010). The efficient use of energy: Tracing the global flow of energy from fuel to service. Energy Policy, 38(1), 75-81.\n", "\n", "MacKay, D. (2008). Sustainable Energy-without the hot air. UIT cambridge.\n", "\n", "Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.\n", "\n", "Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of go without human knowledge. nature, 550(7676), 354-359.\n", "\n", "Silver, D. (2015). Lecture 1: Introduction to reinforcement learning. Google DeepMind, 1, 1-10.\n", "\n", "van Zalk, J., & Behrens, P. (2018). The spatial extent of renewable and non-renewable power generation: A review and meta-analysis of power densities and their application in the US. Energy Policy, 123, 83-91.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }