Video Games as a Testbed for Open-Ended
Phenomena
Sam Earle

Julian Togelius

L. B. Soros

Tandon School of Engineering
New York University
Brooklyn, USA
sam.earle@nyu.edu

Tandon School of Engineering
New York University
Brooklyn, USA
julian@togelius.com

Cross Labs
Cross Compass, Ltd.
Tokyo, Japanlisa.soros@cross-compass.com

Abstract—Understanding and engineering open-endedness, orthe indefinite generation of novelty and complexity at arbitraryscales, has long been studied by implementing nature-inspired
simulations specifically designed for artificial life studies. This
paper argues that video games serve as a complementary domain
for research on open-endedness. In support of this claim, experiments in this paper evaluate the effects of age-based and spatial
destructive events in two game domains: an interactive Gameof Life and the city-building game SimCity. These games areplayed by a neural-network-controlled gameplay agent trying tomaximize reward. Results indicate that experiments with SimCity
are more likely to identify statistically significant differences incomplexity as a result of applied destructive events, highlighting
the utility of this game domain for studying artificial life
phenomena.

I. I NTRODUCTION
Identifying mechanisms that enable open-endedness is afundamental question for the artificial life (ALife) community[1]. In fact, this question has implications reaching beyond
ALife, as [2] hypothesize it may be critical not only forachieving ever-complexifying digital evolution, but also for
creating a path to artificial general intelligence. The basic
goal of open-endedness research is to make complexity evolve
indefinitely and at arbitrary scales.1 Towards this goal, it
is instructive to understand how complexity and robustnesschange over time in response to phenomena observed innature. This paper focuses on catastrophic destructive eventsin particular, which have proven capable of both spurring
evolutionary change and reversing evolutionary expansions of
biological diversity [4], but are not well-studied in the context
of artificial life.
The history of ALife is rich with systems designed toachieve open-endedness, including virtual worlds such as
Tierra [5], Avida [6], Polyworld [7], Geb [8], Division Blocks[9], Evosphere [10], and Chromaria [11]. All of these virtualworlds can be thought of as zero-player video games. Thoughsuch simulations explicitly designed to recapitulate featuresof the natural world have historically served as experimental
domains for open-endedness, other types of domains could
This work was supported by the National Science Foundation (Award number 1717324 - RI: Small: General Intelligence through Algorithm Invention
and Selection).
1 For a more thorough and nuanced discussion of OEE definitions, see [3],Chapter 2.

also serve as viable testbeds for empirical studies of thephenomenon. In particular, this paper argues that certain video
games incorporate some form of open-ended dynamics, andthat such domains could complement the existing catalogueof ALife worlds.
The goal of the experiments presented in this paper is to use
open-ended video game domains to understand how periodic
destructive events of various types affect the complexity of
evolving spatial systems. Note that catastrophe and destructive
event will be used synonymously in this paper. First, priorwork on destructive artificial evolution is reviewed, followedby an introduction to open-endedness and emergence in games.
The two domains used for empirical study are then introduced
along with the gameplay agent and experimental parameters.
The effects of age-based and spatial destructive events on map
complexity are then analyzed for three different game sizes
and three different destructive event frequencies.
II. BACKGROUNDThis section first introduces open-endedness research inartificial life, then studies of destructive events in artificial
evolution, which have historically been studied in the context
of evolutionary algorithms rather than in ALife simulations.
It then provides a theoretical grounding for the video gamedomains used for empirical study.
A. Artificial life and open-ended evolution
The field of Artificial Life, or ALife, in general aims tosimulate and synthesize living systems at all scales, fromphysics to chemistry to biology (with many possible levelsof description in between). Instead of being tied explicitly
to Earth-like systems, however, artificial life researchers seek
to find viable abstractions of the processes that lead to systems like those observed on earth. Understanding open-ended
evolutionary processes, which can be seen as a specific typeof abstract creative process, is a particularly important major
challenge for the field of artificial life with a rich researchhistory. Framed imprecisely, the goal of open-ended evolution
is to construct a system that could produce interesting things
forever. Of course, one of the difficulties here is that whatis “interesting” is hard to define precisely and objectively;

what counts as interesting varies from domain to domain andobserver to observer.
The term open-ended evolution (OEE) was coined by the
ALife community to describe processes in the spirit of natural
evolution. Though the idea has been actively explored since
its conception, its precise definition is still a contentious topic
without a universally satisfying resolution. For example, OEE
has been variously described as:• “a process in which there is the possibility for an indefinite increase in complexity” [12],• “a system in which components continue to evolve newforms continuously, rather than grinding to a halt whensome sort of ‘optimal’ or stable position is reached” [13],• a system in which the “number of possible types by far
exceeds the number of individuals (copies, sequences,etc.) in a plausible (realistic) population” [14], or• the “on-going and indefinitely creative production of significantly new kinds of adaptive responses to significantly
new kinds of adaptive challenges and opportunities” [15].
This list illustrates the extent to which there is disagreement
about the salient features of evolution. The characterization ofevolution as continuously producing novel forms is one of themore popular definitions [16–18]. It should be noted that thesedefinitions are not necessarily mutually exclusive.

Empirically evaluating the functional difference between thesetwo kinds of systems of emergence thus has implications forboth game studies and complexity studies.

B. Destructive events in artificial evolution

SimCity (Figure 1) is a city-building game designed by Will
Wright and loosely based on system dynamicist Jay Wright
Forrester’s theory of urban dynamics [32]. In this single-player
game, players spend money (which is a finite resource in thereal game, but not in the test domain) to build cities composedof residential, commercial, and industrial zones on a fixed 2D
grid. These zones are then procedurally filled with buildingssuch as houses (which increase the maximum population
possible in the city), offices, and factories. However, zones
will only be filled in if there is a need for development;
there is no need to build more houses than the number ofpeople in the city, for instance. In this way, SimCity is akinto a many-dimensional cellular automaton with an addedinteractive component (i.e. the human player, who is able
to add and remove city components). In addition to zones,
the player can construct growth-enabling components such as
power lines and transportation infrastructure. The goal of thetest environment is eventually to reach targets in certain citywide metrics (including population, traffic, and mayor rating)after a fixed amount of time.
As noted by [33], “by coupling cellular automata and system
dynamics style simulations, behavior [in SimCity] unfolds atboth micro and macro scales, giving the microworld bothfinely detailed texture as well as an overarching organic coherence.” Cities (both real and simulated) can also be thoughtof in terms of self-organization, exhibiting emergent features
such as congestion and segregation2 [34]. This game thus
provides an excellent and novel domain for empirically investigating phenomena observed in real-world complex systems,

Results from a few recent studies of evolutionary algorithms
suggest that intentionally removing individuals from the population can improve algorithm performance by increasing evolvability, where evolvability can mean either response to certain
kinds of adaptive challenges [19–21] or potential for creative
innovation [22–24]. Lehman and Miikkulainen [25] test the
hypothesis that extinction events indirectly select for the ability
to proliferate through vacated niches, thereby speeding up
evolution. This work stems from Palmer and Feldman’s earlier
studies on indiscriminate extinctions in spatially segregatedpopulations [26]. Beyond extinction events, Veenstra et al. [27]
investigate the impact of mortality on evolution’s ability totraverse search spaces while avoiding non-functional regions.
In this paradigm, inspired by biological death, individuals are
removed from the population after a fixed amount of time
regardless of reward.
C. Open-ended games
Taking inspiration from the concept of emergence as usedin complexity science and artificial life, and particularly as
dichotomized into strong and weak forms by [28], Juul [29]formulated a theory of open versus closed games, also framed
as emergent versus progressive. (Note that games in thiscontext refers generally to playable board and video games.)
In an attempt to clarify what is meant by games of emergence
using Juul’s term, Soler-Adillon [30] presents case studies of
two games each capable of different degrees of emergence.
Conway’s Game of Life is selected as representative of thenotion of emergence from the complexity science perspective,
and SimCity is chosen as representative of an emergent game.

III. M ETHODOLOGY
The experiments in this paper investigate the impacts ofdestructive events on reward and complexity of game statesachieved by a reinforcement-learning agent playing adaptations of the representative emergent systems studied by [30].
A. Domain 1: Interactive Game of Life
The Game of Life (GoL) is a classic cellular automaton
composed of cells occupying a discrete 2D grid, where eachcell can either be “dead” or “alive.” At each timestep, eachcell’s state is recalculated based on how many of its neighbors
are dead or alive. This canonical implementation can also bethought of as a zero-player game [31], as can many ALife
worlds. The experimental domain in this paper is an interactive
adapted version that allows an automated game-playing agent
to choose a single cell of the automaton and flip it. Pursuant to
GoL cell update rules, the player’s choices can have cascading
effects on the rest of the evolving system.
B. Domain 2: Gym-city, a SimCity1 reinforcement learning
environment

2 The authors would like to clarify that not all emergent features arenecessarily good features.

Fig. 1: SimCity, an open-ended city-building simulation
game. The (human) player must place industrial, commercial,
and residential zones in configurations that encourage population growth.

complementing traditional artificial life worlds and software
platforms.
Following the API of OpenAI Gym3 , which provides
portable environments for comparing reinforcement algorithms, Earle [35] recently re-packaged SimCity1 (whose codeis freely available online) as a reinforcement learning (RL)
environment nicknamed gym-city. This domain is used for allcity-building experiments in this paper. Note that in the RL
domain, money is not a finite resource (though money could
be limited for future experiments).
C. Game-playing agent
Importantly, there is no evolutionary algorithm implemented
for the purpose of the experiments reported in this paper.
Instead, an artificial neural network (ANN) is trained to build,assuming the role that a human player normally would. The
ANN is then responsible for planning the macro-scale city
structure, with micro-level population dynamics governed by
the aforementioned CA rules.
The ANN used in this study is a special type of convolutional neural network called a fractal network, which was
specially designed to take advantage of spatial relationships
in 2D spaces at multiple scales [35, 36]. A controllable RL
agent is trained, as in [37], to achieve target values in certain
high-level metrics in two CA-based domains: Conway’s Gameof Life (GoL) and SimCity. In each domain, the agent observes
a one-hot encoded image of the map (observing tile-statesrather than pixels in the case of SimCity) and builds or deletes
one cell or structure on the map per step, followed by one
or several ticks of the CA or city-simulation, respectively. In
GoL, the sole target metric is net population in terms of thenumber of living cells on the board. In SimCity, there are
multiple target metrics: the city’s net residential, commercial,
3 https://gym.openai.com/

industrial, and traffic populations; the mayor rating; and thenumber of power plants on the map. We use an algorithm
modeling absolute learning progress with gaussian mixture
models (ALP-GMM) [38] to vary these target metrics andtheir weights over the course of training such that the agent’s
learning progress is maximised. These targets and weights areembedded in the agent’s observation, allowing it to associate
diverse sub-policies with various targets.
During inference in SimCity, high targets are provided forall 4 population types (roughly the maximum possible for eachon a 16 × 16–tile map), a target of 1 power plant, and a
100% approval rating. Traffic has a low weight, making it lesslikely to be advantageous for the agent to exploit the game’s
traffic engine and produce excessive traffic by covering themap with road while providing only a few distant zones. The
weight for residential population is similarly small, making theagent less prone to produce a large unemployed population via
a repetitive, dense pattern of residential zones. The agent isthus encouraged to build with a variety of zones, leading to
inhibitory adjacency effects between zone types, which can
only be addressed by more complex city layouts; and global
population dynamics, which make potential solutions morefragile and less flexible (i.e. when scaled to larger maps, they
are prone to violent global population in- and out-fluxes).
The agent plays on maps produced by the game’s built-in
procedural terrain generator, and randomly scattered with a
few additional structures, some of which cannot be deleted by
the agent.
In Game of Life, the target population (number of “alive”
cells corresponds to a densely-filled small map. In random
initial layouts, each cell begins alive with 20% probability. In
both domains, population targets do not scale with map size,
so that on larger maps, it is in the agent’s best interest to fillout the map only partially, providing a novel generalization
challenge.
To produce the data in Figures 2–3, the best-performingcolumn of the trained fractal network is sampled to produce
actions, and the agent plays on a land-only map for 20 episodesof 1,000 steps, after which reward and compressibility tend
to converge. On the largest maps, however, SimCity layouts
nevertheless continue to evolve for longer, so Figure 5 renders
snapshots of gameplay from 3,000–step episodes.
D. Experimental setup
In both domains4 , the gameplay agent plays on three different map scales: 16 × 16, 32 × 32, and 64 × 64. Each run lasts
2000 timesteps. In control runs, the agent is allowed to playwithout interference. In all other runs, one of the following
types of destructive events occurs at fixed intervals (with only
one type of destructive event occurring in a single run):• Random: structures are selected for deletion at random,without replacement• Spatial: as a metaphor for catastrophic geological events,a tile is selected at random from the map, and, spiralling
4 Source code is available at https://github.com/smearle/gym-city

out from this central tile, any structure occupying a tilealong this path is deleted• Age: as a metaphor for biological mortality, structures aredeleted in decreasing order of their age (oldest first)
Every step, the raw pixel map is converted to a tile map,and the type of structure occupying that tile determines its
color according to the following functional categories: residential, commercial, industrial, infrastructure (transport, services),
power, and other (natural structures, disaster-related objects
like fire or radioactive waste). JPEG compression is thenapplied, giving an approximation of the complexity of theconstructed cities. The general approach of using compressibility as complexity metric has been previously found to becomparable to more widely-used metrics, such as Shannon
entropy, for detecting major evolutionary changes in cellular
automata [39]. As noted by [40], compression-based proxies
for complexity are not perfect metrics because extreme values
correspond to complete order and complete disorder; it is unclear what intermediate value would be an indicator of “ideal”
complexity. Nonetheless, JPEG compression is able to capture
useful information about structured, spatial relationships.

(a) Average reward

IV. R ESULTS
A. Interactive Game of Life
Figure 2 illustrates the effects over time of destructive events
at various frequencies and at various map sizes on an agent’s
gameplay in interactive Game of Life. On a small map at low
frequency, facing the agent with destructive events leads toconsistently greater average cumulative reward over the courseof the episode. On these small maps where movement towarda denser map-state is rewarded, intermittent mass cell-death
allows the prompts the agent to repeatedly repopulate the map,
allowing it to climb out of local optima. Destructive events
result in empty patches on the map which are reflected in steep
drops in complexity (in terms of jpeg compressibility), thoughagents are able to return quickly to states with complexity
matching that of states on an undisturbed map.
As the frequency of these events increases, average reward
decreases. With excessive destruction, the agent has difficulty
maintaining any living cells, as those remaining are likely
to die from isolation. In the extreme case, this can leadto an irreversibly empty map. This tendency is reflected by
downward trends in measures of both reward and complexity.
On a medium-sized map, the agent is rewarded for moving
toward a population just less than the average on initialization.
But, as on the small map, agents respond to catastrophe by
fostering growth, this time at a detriment to reward. Thistendency is repeated less pronouncedly on the large map,
where destructive events have less impact overall. In bothcases, increasing destructive frequency results in a loss ofcomplexity and slight increase in reward as destructive events
become more difficult to counteract with growth.
B. Gym-city
Figure 3 illustrates the effects of destructive interference on
average attained reward and inverse JPEG compressibility over(b) Average inverse JPEG compressibility

Fig. 2: Results for Interactive Game of Life. Averages
are calculated over 40 runs. The table below each subfigure
gives p-value from a Mann-Whitney test. Green boxes indicate
statistically significant differences (p < 0.05).

40 trials. Overall, catastrophic events tend to decrease, rather
than increase, reward. This result is likely due to factors suchas (intended) sudden destruction of populated zones, resulting
in large dips in population, instability in neighboring zones(via disrupted desirability effects) and potential holes in the
power grid. More frequent destruction events leads to moresuch destruction, and thus a greater negative affect on reward.
In any case, in most cases, particularly on small and medium
maps, age-based and spatial destruction are not significantly
different from random destruction.

On large maps with infrequent distinction, however, spatial and random destruction lead to greater reward than onundisturbed maps. The city-building agent, having been trainedon small maps, is unsuccessful on larger maps, leaving otherwise feasible residential neighborhoods disconnected frompower (figure 4). Persistent (but non-overwhelming) destructive events disrupt this flawed stable state, and in forcing
the agent to repair its broken map, lead to various other,more successful states, with dynamics that continue to evolvefor several thousand time-steps. Spatial destruction (figure5a) produces a constantly-migrating city center (comprising a
power plant, a residential center, and an industrial periphery),while age-based destruction (figure ??) produces a map thatis constantly rippling or scanning upward, from left to right;
destructive events, echoing past builds, leave horizontal bandsof empty space on the map, which are repeatedly filled-in to
produce various artefacts. In both cases, the irregular patterns
resulting from destruction lead to greater and more robust
connectivity (in terms of power) relative to the undisturbed
map.
Complexity results in this domain are more informative withrespect to evolutionary dynamics (and have a higher potential
for cross-domain generalizability). Here, there are significant
differences among most types of destructive events, though
the large map is less able to discriminate between different destructive event types. On other map sizes, introducing
destructive events tends to lead to increased file size after
compression. Bearing in mind that compression size is nota perfect method for measuring complexity, it is not possible
to say whether the achieved complexity is “ideal” or not, onlythat there is positively an increase. It should also be noted that
there is a significantly higher variance between runs on large
maps with lower frequencies of destructive events, suggestingthat other parameter combinations are more predictable.
Of course, for the purpose of both validating the chosen
metrics and gaining a better understanding of what’s actually
happening in the game, it is helpful to examine actual maps
constructed by the game-playing agent. Figure 4 shows the
final game state achieved by agent in the control scenario,
where no catastrophes are introduced. Figure 5 depict representative runs subject to age-based and spatial catastrophes,
respectively.

(a) Average reward

V. D ISCUSSION(b) Average inverse JPEG compressibility

In the Interactive Game of Life domain, age-based interventions are most likely to lead to all-out extinction, likely because
older cells are more likely to remain alive than younger ones.
An old cell is often part of some stable or partially oscillating
multi-cellular configuration, while a new one is more likely
to disappear on the next tick (or within a few) as it may
be part of some travelling or oscillating configuration. The
deletion of older cells would thus have a larger impact on
future population counts (if only for the absence of the deleted
cell itself from future time-steps). Spatial events are least likely
to lead to all-out extinction, likely because, even when onlya small number of cells are left after an event, these cells are

Fig. 3: Results for SimCity. Averages are calculated over
40 runs. The table below each subfigure gives p-value froma Mann-Whitney test. Green boxes indicate statistically significant differences (p < 0.05). Destructive events generally
lead to decreased reward, but more complexity (less compressibility) after 1000 timesteps. On large maps with infrequent
destruction, they lead to increased reward.

Fig. 4: Stable state of a representative city built on large
and small (lower-right cornder) tile maps by an RL agent,
without destruction. On a large map, the agent plans what
would be an effective residential neighbourhood, but fails toconnect it to power. This primarily residential pattern is also
observed in control experiments on the small and medium
maps.

more likely to be grouped together on the map than under
different types of destruction, which may thin out populations
more uniformly over the board.
With destruction in gym-city, the agent is more likely todeviate from the random start and use regular building patterns
to achieve the target population. This is especially common inage-based destruction, where, during the first event, the oldest
cells (and those to be deleted) are precisely those belonging to
the random starting configuration.The resulting, more regular
final configurations, are more compressible.
Interestingly, the differences between catastrophe types withrespect to complexity tend to be more statistically significant in
gym-city than in Interactive Game of Life. When considering
the difference between emergence in the traditional complex
systems sense and emergence in games, one might conclude
that SimCity is a better testbed for empirical studies of discrete
dynamical systems, or at least a complementary one that ALife
researchers should consider adding to their digital toolboxes.
An important and interesting question to consider is how
the concept of openness in games corresponds to notions of
open-endedness in artificial life. At a high level, the goals
are the same: to construct a source of unlimited innovationand opportunity for new strategies, or ways of being, that are
discovered rather than designed. However, one aspect of openended evolution (in the ALife sense) that is not captured in
at least Jesper Juul’s framework is the concept of a shift inindividuality or a major/phase transition [41], the lack of which

may be a barrier to open-ended evolution [42].
Many open-ended games implicitly or explicitly supportand encourage some kind of predetermined progression. Thisapplies to putatively open-ended first-person games such as
Minecraft (which in most game modes have some formof quests) and No Man’s Sky (which has a very thin but
existing questline, and a rather complex progression system).
Even third-person open-ended “god games” have some strong
steering mechanisms built in. For example, the games in
the Civilization series have victory conditions, and most ofthem finish at a predetermined point. By virtue of being
artefacts designed for a particular kind of human-computerinteraction (which we for lack of a better expression cancall interactive entertainment, or simply “gameplay”), thesegames offer restrictions, rewards and affordances that push
gameplay in a particular direction. It is thus not clear exactly
what would constitute an “unbiased” open-ended game. Yet,
open-ended evolution (both in nature and in algorithmic form)
also necessarily entails some sort of progression, otherwise it
would degrade to exploration of a relatively simple possiblity
space.
Capturing the spirit of open-endedness in an algorithmic
process has long been a focus of the artificial life community.
While the exact nature of open-endedness research is difficult
to articulate precisely, it essentially aims to replicate the chainof endless innovation achieved by biological evolution innature. The games community, too, has similarly long aimed todevise such creative algorithmic processes for the purpose ofi.e. procedural content generation. The 2016 space exploration
game No Man’s Sky is an example of such an endeavor; theworld consists of over 18 quintillion automatically generated
planets each rife with reasonably unique flora and fauna. As
described in the New Yorker, “Because the designers arebuilding their universe by establishing its laws of nature,
rather than by hand-crafting its details, much about it remains
unknown, even to them.” [43] Unfortunately, the core PCG
algorithm was incapable of producing new kinds of things,
resulting in what Kate Compton calls the 10,000 Bowls of
Oatmeal Problem [44] wherein generated artifacts may bemathematically distinct (like oats at different positions andorientations in a bowl), but are not distinct in any perceivableor meaningful way.
In artificial life, the salient question is about what constitutes
an individual in any particular system. In many virtual ALife
worlds, an individual is an embodied agent that moves aroundin a fixed world sometimes with resources that must becollected and predators that must be avoided. This level ofdescription or individuality is commensurate with biology inthe natural world and is reminiscent of many video games.
Innovation, then, occurs with respect to morphologies andbehavioral strategies. It is also possible to define individuality
at a much lower level, such as that of an atom or molecule,which is the approach in artificial chemistry. Frankly, here’s
where things get really interesting for games in particular,
as it’s less clear what constitutes a fundamental “unit” ofgameplay that could be combined in an indefinitely scalable

timestep = 1000

timestep = 3000(a) Snapshots from a sample gym-city run on a 64 × 64 map with spatial catastrophes occurring every 100 steps. The usual grid isleft incomplete, with lateral connections appearing at destruction sites. Over large time-scales, the residential core and industrial periphery
migrate across the map as a result of continued disturbance.

timestep = 1000

timestep = 3000(b) Snapshots from a sample gym-city run on a 64 × 64 map with age-based catastrophes occurring every 100 steps. Large scale anddynamic patterns emerge as the agent’s own actions leave a disruptive wake. A band of industrial zones repeatedly ”scans” the map upward(mirroring the upward, left-to-right build pattern of the initial residential zones), and a growing cluster of road appears in the center of themap.

Fig. 5: Both spatial and age-based destruction allow an RL agent to explore more complex and diverse states over large
timescales and maps. Age-based catastrophes lead to reward comparable with an undisturbed agent.

way, as in the natural world. Still, we haven’t yet been able toovercome what might be called the 10,000 bowls of Primordial
Soup Problem.
At the beginning of this paper, we argued that video games
can potentially serve as complements to open-endedness research in ALife worlds. Clearly, there is some fuzzy groundat exactly the locus of simulation games such as SimCity,
where the goal is to control and environment rather than toparticipate in it immersively. If nothing else, the fact thatcompressibility metrics capable of detecting phase transitionsin natural systems can be applied to game states should makeus consider more seriously where the boundaries between
these disciplines truly lay and whether we can all benefit fromeach other’s insights.
VI. C ONCLUSION
This paper investigated the effects of age-based and spatial
catastrophic events on cellular-automata-based video games
capable of different degrees of emergence. Results showedthat spatial catastrophes tend to have a more dramatic effecton system quality and complexity than age-based catastrophes,
but that either type of destructive event has a stronger effectthan random destructions or none at all. More broadly, theseexperiments provided an initial demonstration of video games
as a complementary domain to artificial life for exploring
phenomena related to open-endedness.
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