Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game dynamics, and goals presented to an agent. The last two years have seen a spate of research into artificial agents that use a single algorithm to learn to play these games. The best of these artificial agents perform at better-than-human levels on most games, but require hundreds of hours of game-play experience to produce such behavior. Humans, on the other hand, can learn to perform well on these tasks in a matter of minutes. In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mechanisms that lead to such rapid learning.National Science Foundation (U.S.) (Award CCF-1231216

Gershman, Samuel J.

Pouncy, Thomas

Tenenbaum, Joshua B

Tsividis, Pedro

Xu, Jacqueline L.

DSpace@MIT

Human Learning in AtariPedro A. TsividisDepartment of Brain and Cognitive SciencesMITThomas PouncyDepartment of PsychologyHarvard UniversityJacqueline L.XuCSAILMITJoshua B. TenenbaumDepartment of Brain and Cognitive SciencesMITSamuel J. GershmanDepartment of Psychology and Center for Brain ScienceHarvard UniversityAbstractAtari games are an excellent testbed for studying intelligentbehavior, as they offer a range of tasks that differ widely intheir visual representation, game dynamics, and goals pre-sented to an agent. The last two years have seen a spate ofresearch into artificial agents that use a single algorithm tolearn to play these games. The best of these artificial agentsperform at better-than-human levels on most games, but re-quire hundreds of hours of game-play experience to producesuch behavior. Humans, on the other hand, can learn to per-form well on these tasks in a matter of minutes. In this pa-per we present data on human learning trajectories for severalAtari games, and test several hypotheses about the mecha-nisms that lead to such rapid learning.IntroductionReinforcement learning algorithms using deep neural net-works have begun to surpass human-level performance oncomplex control problems like Atari games (Guo et al. 2014;Mnih et al. 2015; Van Hasselt, Guez, and Silver 2015;Schaul et al. 2015; Stadie, Levine, and Abbeel 2015). How-ever, these algorithms require hundreds of hours of game-play to achieve human levels of performance, while our ex-periments show that humans are able to learn these tasksin a matter of minutes. This suggests that these algorithmsmay employ different representations and learning mecha-nisms than humans. Many decades of research in cognitivescience has shown that humans have early-arising ”start-up”software – rich representations about objects and physics(Spelke 1990; Baillargeon 2004; Baillargeon et al. 2009;Rips and Hespos 2015) and about agents (Johnson, Slaugh-ter, and Carey 1998; Tremoulet and Feldman 2000; Csibra etal. 2003; Schlottmann et al. 2006; Spelke and Kinzler 2007;Csibra 2008; Kiley Hamlin et al. 2013), and that humanshave the capacity to rapidly acquire new concepts (Carey1978; Landau, Smith, and Jones 1988; Markman 1989;Bloom 2000; Xu and Tenenbaum 2007; Lake, Salakhutdi-nov, and Tenenbaum 2015) and build intuitive theories (Mur-phy and Medin 1985; Carey 1985; Gopnik, Meltzoff, andBryant 1997), which they can use to explain (Lombrozo2009; Williams and Lombrozo 2010), predict (Rips 1975;Murphy and Ross 1994), and imagine (Ward 1994; Jern andCopyright c© 2017, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.Kemp 2013). For a thorough review of the human ”start-up”software, see Lake et al. (2016).In addition to bringing early-arising representations tobear on Atari tasks, humans come equipped with rich priorknowledge about the world – for example, knowledge aboutkeys, doors, ice, birds, and so on. While such knowledgecould give humans an edge over AI algorithms, we believethat it plays a minimal role in humans’ ability to rapidly mas-ter these tasks. Instead, we believe that strong priors speci-fied at a more general level are what give rise to rapid learn-ing. This includes priors about objects as spatiotemporallycontiguous entities whose properties can be learned from ex-perience; an imperative to explore these objects and to ob-serve available evidence to rapidly build theory-like mod-els of the Atari worlds they encounter; and an ability to usethese models to simulate possible future worlds and generateeffective plans.In this paper we present some of the first systematic dataon human Atari gameplay. Going beyond simply observinggameplay, we experimentally manipulate (1) prior knowl-edge about specific objects, (2) prior knowledge about thegame environment and rules, and (3) observational learningexperience. These manipulations allow us to test differenthypotheses about the nature of human learning in Atari.Human Atari GameplayPart I - GameplayMethods We selected two games in which humans out-perform the asymptotic Double Deep Q-Network (DDQN)(Frostbite and Venture), and two games in which the DDQNoutperforms humans (Amidar and Stargunner). Participantsfound through Amazon Mechanical Turk were assigned toone game that they had not previously played, and were toldthat they should play for a minimum of 15 minutes. All par-ticipants were paid $2.00 and were promised bonus pay ofup to $2.00 extra depending on their cumulative score per-formance. Prior to playing the game, participants were toldonly that they could use the arrow keys and the spacebar, andthat, beyond that, they should try to figure out how the gameworked in order to play well. Participant numbers are as fol-lows: (Frostbite: 71, Venture: 18, Amidar: 19, Stargunner:19).Figure 1: Human learning curves for four Atari games. Black horizontal line: random play. Blue horizontal line: ‘expert’play. Red horizontal lines: DDQN after 10, 25, and 200 million frames of game-play experience (46, 115, and 920 hours,respectively).Learning Curves Figure 1 shows learning curves for thefour games. Each points represents a (human, time, score)tuple; the score reported at each point is the highest score ob-tained by that particular subject after that amount of cumu-lative gameplay experience. For comparison, we have alsoplotted random performance (black horizontal), human ‘ex-pert’1 play (blue horizontal line), and DDQN performanceafter 10, 25, and 200 million frames of game-play experi-ence (46, 115, and 920 hours, respectively), in red (bottomto top)2. We highlight a few qualitative observations: hu-man performance is above random performance within thefirst minute of play; in three out of the four games, humansreach ‘expert’ performance within the allotted 15 minutes(on Amidar they are well on their way); in all of the games,humans exceed the DDQN’s 10- and 25-million-frame scorewithin just a few minutes; in Frostbite and Venture, as men-tioned earlier, humans exceed even the DDQN’s asymptoticperformance.Of course, the 10-million (46-hr) and 25-million-frame(115-hr) comparisons are unfair – after all, in additionto having potential cognitive advantages, humans come tothese tasks with a working visual system, while the DDQNhas to learn a visual system from scratch. With this in mind,we can also look at the DDQN’s rate of improvement andcompare it to human rates of improvement at score-matchedpoints – including points at which the DDQN is doing rel-atively well. Figure 2 shows such a comparison for three ofour four games; a comparison for Venture is missing becausehumans immediately exceeded the DDQNs performance inthe 10-40 million frame range. Note that the rate of improve-ment is measured in log units.1‘Expert’ refers to the human game tester employed by Deep-Mind and used as the human benchmark in the original DQN paper.After the tester trained on each game for two hours, their scoresover the 20 subsequent game episodes that lasted over 5 minuteswere averaged and reported.2Data taken from Schaul (2015). As of this writing, this wasthe highest-performing Deep RL model for which we could ob-tain learning-curve-like data for a large range of games. The modelsignificantly outperforms the original DQN, as well as several sub-sequent variants.Figure 2: Learning rate comparison. X-axis: DDQN experi-ence, in millions of frames. Y-axis: Rate of improvement inlog (points per minute), estimated using finite differences.Human rate of improvement is taken from score-matchedpoints (shown as numbers annotating the curves). A compar-ison for Venture is missing because humans immediately ex-ceeded the DDQN’s performance in the 10-40 million range.DDQN estimates are made from Figure 7 in Schaul et al.(2015).Part II - Experimental ManipulationsThe game of Frostbite is one of several games worth analyz-ing closely, as humans exhibit particularly impressive per-formance relative to the DDQN. In what follows we presentseveral experimental manipulations. Our hope is that by un-derstanding the representations that facilitate human perfor-mance in this game (and eventually in other games), we canhelp pave the way toward designing more human-like AIagents.Figure 3: Screenshot of a blurred version of Frostbite. Thelight blue items are birds; the green items are fish.Obscuring Object Identity One specific hypothesis as towhy humans rapidly learn to perform well at Frostbite is thatthey come equipped with strong priors about the objects theyencounter. Parsing the game screen into platforms, igloos,birds, and fish must clearly make the task easier for humans,as humans know things about the properties of each of theseobjects: platforms provide support; igloos provide shelter;fish and birds can be eaten. To test whether such knowledgeis in fact a benefit to humans, we created a blurred versionof the game, in which objects could be only be identified asgeneric objects – that is, we masked the semantic identity ofthe objects.3 Figure 3 shows an example game screen. Wepresent data from this condition after describing the otherexperimental manipulations.Reading the Instruction Manual If people’s rapid learn-ing is due to the formation of a model-like representation,then anything that would enable them to learn this represen-tation should result in an increase in early performance. Totest this hypothesis, we provided participants with the oppor-tunity to read the game’s original instruction manual prior toplaying. The intent was to provide players with a short de-scription of critical aspects of gameplay, which was mostlyinformative about objects and their roles in the game, as wellas the goal of the game. Subjects read the manual, answereda short questionnaire intended to check that they understoodthe rules, and then played for 15 minutes.Learning from Observation Humans’ ability to form atheory-like representation of the game should also be aidedby observing others play. We randomly selected an episodein the 75-85th percentiles of episodes from the first round ofexperiments, and had all participants in this condition watcha video of that episode prior to playing. The episode corre-sponded to the 79th percentile of all Frostbite episodes, andlasted 1 minute, 26 seconds.Results Figure 4 shows means and 95% CIs of first-episode scores for normal, ‘blur’, ‘instructions’, and ‘obser-3This resulted in blurred birds, fish, crabs, and clams, butclearly-identifiable water, ice floes, and igloos.Figure 4: First-episode mean scores and 95% confidenceinterval for normal, ‘blurred’, ‘instructions’, and ‘observa-tion’ condition. Note that blurring the screen has no effect,whereas the instructions and observing another player al-low humans to capture approximately 1000 points in theirfirst episode – this corresponds to human performance afterabout 5 minutes of play under normal conditions.vation’ conditions (participant Ns are 71, 63, 72, 72, respec-tively). The difference in first-episode performance betweennormal and blurred conditions is not significant (p=0.663.Mean, CIs: Normal: 356, [167, 545]. Blur: 417, [216, 618]).This is unsurprising: a bird, a priori, could be useful (it canbe hunted and eaten) or it could be harmful (it can attackyou). In the actual game the interaction between the agentand the bird is, in fact, quite implausible a priori – real-lifebirds are much lighter than humans and are generally notcapable of pushing them, and yet, in the game they do so.While priors about object identity and the resulting behav-iors of objects may be minimally useful to a novice player,most of the important properties of objects in these gamescome from their role in the particular game. It is after ob-serving objects’ movements and interactions that humansrapidly form theories of the game dynamics.As we expected, reading the instruction manual and ob-serving competent players provided participants with a sig-nificant first-episode advantage over normal play (Instruc-tions vs. Normal: p=.0001. Observation vs. Normal: p=.002.Mean, CIs: Normal: 356, [167, 545]. Instructions: 1848,[1144, 2552]. Observation: 1144, [683, 1605]). Had play-ers only learned from observation, one might posit that theysimply copied a successful policy. However, the fact that theinstruction manual was helpful suggests that this is not thecase, and hints at the possibility that humans used this infor-mation to build a model of the game dynamics, which theythen used to play successfully.DiscussionThe real power of human intuitive theories is that they enablehumans to explain the world, generalize from few examples,think counterfactually, and generate effective plans. 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Human learning in Atari

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