Ny mpanoratra: Scott Reed Konrad Żołna Emilio Parisotto Sergio Gómez Colmenarejo Alexander Novikov Gabriel Barth-Maron Mai Giménez Yury Sulsky Jackie Kay Jost Tobias Springenberg Tom Eccles Jake Bruce Ali Razavi Ashley Edwards Nicolas Heess Yutian Chen Raia Hadsell Oriol Vinyals Mahyar Bordbar Nando de Freitas Ny mpanoratra: Avy amin'i Reed Ny vadiny dia Ny vadiny dia Parisotto. Mpanoratra anarana: Sergio Gómez Colmenarejo. Ny renivohiny dia Novikov. Sary avy amin'i Gabriel Barth-Maron. Amin'ny ankapobeny Mifanohitra amin'i Sulsky Ny renivohiny Ny renivohiny dia Tobias Springenberg. Ny Tom Eccles. Ny renivohiny Izany no mahatonga Ny vadiny dia Ashley Edwards. Ny renivohiny dia Ary ny Chen Mifindra ao amin'ny Ny Vinyal dia Mifototra amin'ny Ny renivohiny dia Ny Abstract Miankina amin'ny fandrosoana amin'ny famolavolana fiteny lehibe, dia mampiasa fomba mitovy amin'ny famoronana solontenan'ny solontenan'ny solontenan'ny solontenan'ny lahatsoratra isika. Ny solontenan'ny, izay antsointsika hoe Gato, dia miasa toy ny politika multi-modal, multi-task, multi-embodiment generalist. Ny tambajotra mitovy amin'ny lanjany dia afaka milalao Atari, sary, chat, blôks amin'ny tanana robot tena sy bebe kokoa, manapa-kevitra miorina amin'ny kontekst ny famoahana lahatsoratra, torque, bokotra tsindrio, na tokens hafa. Ao amin'ity tatitra ity dia mamaritra ny modely sy ny angon-drakitra ary manaporofo ny 1 Ny fampidirana Misy tombontsoa goavana amin'ny fampiasana modely famaritana neuronal tokana iray amin'ny asa rehetra. Mampihena ny ilaina amin'ny modely politika amin'ny famolavolana ara-dalàna miaraka amin'ny fiantraikany ara-toekarena tsara ho an'ny sehatra tsirairay. Manatsara ny habetsaky ny angon-drakitra fampiofanana sy ny fahasamihafana satria ny modely famaritana dia afaka mandray angon-drakitra rehetra izay azo soratra amin'ny famaritana tokana. Ankoatra izany, ny fahombiazan'izy ireo dia mitohy amin'ny fanatsarana na dia eo amin'ny sisin'ny angon-drakitra, solosaina sy modely. Amin'ny ankapobeny, ny modely ankapobeny izay tsara kokoa amin'ny fampiasana solosaina ihany koa dia niakatra mihoatra noho ny fomba fanao manokana amin'ny sehatra manokana. Amin'ny farany (Ary amin'ny alàlan'ny fahasamihafana, Ny taona 2020 Ny Tompo sy ny mpiara-miasa. Ny taona 2022. Ary ny Ny taona 2019, Ao amin'ity lahatsoratra ity, dia mamaritra ny iteration amin'izao fotoana izao amin'ny manam-pahaizana ankapobeny izay antsointsika hoe Gato, indraindray ho toy ny modely iray, lehibe, transformer sequence. Amin'ny alalan'ny singa iray amin'ny lanjany, Gato dia afaka mandray anjara amin'ny fifanakalozan-kevitra, sary an-tsoratra, hametraka bloky amin'ny tanana robot tena izy, mihoatra noho ny olombelona amin'ny lalao Atari, mandehandeha amin'ny tontolo iainana 3D, manaraka ny torolàlana, ary bebe kokoa. Na dia tsy azo heverina aza fa tsy misy mpitantana hanana fahombiazana amin'ny asa fanaraha-maso rehetra azo eritreretina, indrindra fa ireo lavitra noho ny fampielezan-kevitra amin'ny fampiofanana azy, dia manandrana ny fanapahan-kevitra izay mampiofanana mpitantana izay matetika dia afaka amin'ny fampiofanana. of tasks is possible; and that this general agent can be adapted with little extra data to succeed at an even larger number of tasks. We hypothesize that such an agent can be obtained through scaling data, compute and model parameters, continually broadening the training distribution while maintaining performance, towards covering any task, behavior and embodiment of interest. In this setting, natural lan-guage can act as a common grounding across otherwise incompatible embodiments, unlocking combinatorial generalization to new behaviors. Ny isan'ny Mifantoka ny fampiofanana amin'ny dingana miasa amin'ny endriky ny modely izay mamela ny fanaraha-maso amin'ny tontolo tena amin'izao fotoana izao ny robots, amin'izao fotoana izao manodidina ny 1.2B parameters amin'ny tranga Gato. Araka ny fanatsarana ny fitaovana sy ny endriky ny modely, ity dingana miasa ity dia hampitombo ny habetsahan'ny modely azo atao, mampitombo ny modely ankapobeny avo lenta ny lalàmpanorenana. Ho an'ny tsotra Gato dia nianatra an-tserasera amin'ny fomba voafehin'ny; Na izany aza, amin'ny fitsipika, tsy misy antony tokony tsy afaka hianatra na amin'ny aterineto na amin'ny aterineto fianarana fanamafisana (RL). 2 Ny modely Ny fototry ny famolavolana an'i Gato dia ny fampiofanana ny karazana angon-drakitra manan-danja araka izay azo atao, anisan'izany ny fomba samihafa toy ny sary, lahatsoratra, proprioception, torque, tsindry tsindry, ary fanadihadiana sy hetsika hafa tsy tapaka sy tsy tapaka. Mba ahafahan'ny fanodinana ity angon-drakitra multi-modal ity, dia manara-maso ny angon-drakitra rehetra amin'ny fametrahana token. Ao amin'ity fampisehoana ity, Gato dia azo fampiofanana sy ny sampana avy amin'ny fomba mitovy amin'ny modely fiteny maromaro mahazatra. Nandritra ny fampiharana, ny token tsindry dia naorina ho valin'ny dialoha, 2.1 Ny famolavolana Misy fomba be dia be azo atao mba hanova ny angon-drakitra ho token, anisan'izany ny fampiasana mivantana amin'ny fampiasana ny basin'ny byte.Ato ambany dia manamarika ny fandaharam-potoana tokenization izay hitantsika mba hiteraka ny vokatra tsara indrindra ho an'ny Gato amin'ny habetsaky ny fitaovana sy ny rafitra modely ankehitriny. Ny lahatsoratra dia voasoratra amin'ny alàlan'ny SentencePiece (Kudo & Richardson, 2018) miaraka amin'ny teny fanalahidy 32000 ao amin'ny sehatry ny ankapobeny [0, 32000]. Ny sary dia miova voalohany amin'ny andian-dahatsoratra tsy mifanaraka amin'ny 16 16 patches amin'ny fe-potoana, araka ny nataony ao amin'ny ViT (Dosovitskiy et al., 2020). Ny piksel tsirairay ao amin'ny sary __p__atches dia avy eo normalized eo anelanelan'ny [−1*,* 1] ary mizara amin'ny firaketan'ny patch size (ohatra √16 = 4). Ny vidin'ny diskreta, ohatra ny tsindry Atari, dia voasokafana amin'ny andian-dahatsoratra amin'ny fe-potoana lehibe. Ny vokatra tokenized dia andian-dahatsoratra amin'ny andian-dahatsoratra ao amin'ny sehatry ny [0*,* 1024). Ny lanjany tsy tapaka, ohatra ny fidirana proprioceptive na ny torque mifandray, dia voasokafana voalohany amin'ny andian-dahatsoratra ny lanjany amin'ny lanjany lehibe. Ny lanjany dia voasokafana amin'ny sehatra [ 1*,* 1] raha tsy misy (jereo ny antsipiriany ao amin'ny sary 14), ary avy eo dia voasokafana amin'ny 1024 tokan-javatra mitovy. Taorian'ny famindrana ny angon-drakitra ho token, dia mampiasa ny fametrahana famaritana canonical manaraka isika. Text tokens amin'ny fehezan-dalàna mitovy amin'ny taratasy voasoratra. Sary patch tokens amin'ny raharaham-barotra. Ny tsindry dia amin'ny fe-potoana lehibe. Ny fametrahana ny rafitra ao amin'ny lisitry ny lexicographical amin'ny fanalahidy. Agent timestops toy ny fanadihadiana token manaraka ny separator, ary avy eo ny hetsika token. Mifandray amin'ny mpandray anjara amin'ny fotoana araka ny fe-potoana. Ny antsipiriany bebe kokoa momba ny antontan-taratasy tokenizing dia omena ao amin'ny fitaovana fanampiny (Section Ny B. 2.2 Ny fametrahana token fidirana sy ny fametrahana tanjona fidirana Taorian'ny tokenization sy sequencing, dia mampihatra ny endri-javatra fiterahana *f* ( ; *θe*) ho an'ny token tsirairay (ohatra, dia ampiharina amin'ny fanadihadiana sy ny hetsika) mba hahazoana ny modely farany entana. • Token izay ahitana lahatsoratra, fanehoan-kevitra na hetsika tsy tapaka na tsy tapaka ho an'ny dingana fotoana rehetra dia napetraka amin'ny alàlan'ny tabilao fikarohana ao amin'ny sehatry ny fampianarana vektor. Ny fampianarana toerana dia voafetra ho an'ny token rehetra mifototra amin'ny toerana token eo an-toerana ao anatin'ny dingana fotoana mifandraika. • Tokens izay anisan'ny sary patches ho an'ny fotoana rehetra dia tafiditra amin'ny fampiasana ResNet iray Ho an'ny sary patch token embeddings, dia manampy ihany koa ny fampianarana ao anatin'ny sary toerana encoding vektor. Izy sy ny hafa. Ny taona 2016A Manondro ny fizarana ao amin'ny Appendix Ho an'ny antsipirihany feno momba ny fonosana embedded. Ny C3 Rehefa modely ny angon-drakitra autoregressively, ny token tsirairay dia mety ho marika tanjona ihany koa raha oharina amin'ny token teo aloha. Token lahatsoratra, vidin'ny tsy misy dikany sy tsy misy dikany, ary ny hetsika dia azo napetraka mivantana ho tanjona aorian'ny tokenization. Token sary sy ny fanehoan-kevitra tsy misy dikany amin'izao fotoana izao dia tsy voafaritra ao amin'ny Gato, na dia mety ho lalana mahafinaritra ho an'ny asa ho avy. Ny tanjona ho an'ireo token tsy voafaritra ireo dia napetraka amin'ny vidiny tsy ampiasaina ary ny fandraisana anjara amin'ny fahaverezan'izy ireo dia voamarina. 2.3 Ny fampiofanana Araka ny fanehoan-kevitra momba ny token Ny 1 : Ny fitsipika , dia mamolavola ny angon-drakitra amin'ny fampiasana ny fitsipika ahitana ny mety: s L θ Let indices ny fampiofanana andian-dahatsoratra. dia mamaritra ny fampiofanana fonosana *m* toy izany fa *m* (*b, l*) = 1 raha ny token amin'ny indices *l* dia avy amin'ny lahatsoratra na avy amin'ny hetsika voasoratra avy amin'ny solosaina, ary 0 raha tsy izany. Ny fahaverezan'ny fampiofanana ho an'ny andian-dahatsoratra *B* dia azo soratana toy ny b Araka ny voalaza etsy ambony, ny rafitra Gato ny tambajotra dia manana singa roa fototra: ny parameterized embedding asa izay manova token ho token embeddings, ary ny andian-dahatsoratra modely izay avy amin'ny famoahana ny famoahana ny manaraka token miavaka. ho an'ny tsotra sy ny fahafaha-mahazatra. Gato mampiasa 1.2B parameter decoder-only transformer amin'ny 24 layers, embedding size 2048, ary post-attention feedforward hidden size 8196 (manampahafantatra bebe kokoa ao amin'ny fizarana Nandray anjara tamin'ny fanatanterahana ny Ny taona 2017 Ny 1). Satria ny asa samihafa ao anatin'ny sehatra iray dia afaka mizara fampiharana mitovy, endrika fanadihadiana ary fepetra famaritana, ny modely indraindray dia mila fifandraisana bebe kokoa mba hahafantarana ny asa. ary mampiasa fanomanana haingana. Nandritra ny fampiofanana, ho an'ny 25% amin'ny andian-dahatsoratra ao amin'ny tsirairay, ny andian-dahatsoratra haingana dia voamarina, avy amin'ny fizarana nateraky ny loharano mitovy amin'ny loharano iray amin'ny asa mitovy. Ny antsasaky ny andian-dahatsoratra haingana dia avy amin'ny faran'ny andian-dahatsoratra, miasa toy ny endrika fanomanana tanjona ho an'ny sehatra maro; ary ny antsasany hafa dia voamarina avy amin'ny andian-dahatsoratra. Ao anatin'ny fanombanana, ny mpamatsy dia azo voamarina amin'ny fampiasana ny fampisehoana mahomby ny asa ilaina, izay ataontsika amin'ny ankapobeny amin'ny (Ny Tompo sy ny olon-drehetra) Ny taona 2022. Ny Tompo sy ny hafa. Ny taona 2021. Brown sy ny hafa. Ny taona 2020 Ny fampiofanana ny modely dia atao amin'ny 16x16 TPU v3 slice ho an'ny dingana 1M miaraka amin'ny habetsahan'ny batch 512 sy ny fe-potoana token = 1024, izay maharitra manodidina ny 4 andro. Ny antsipiriany momba ny fanorenana dia azo jerena ao amin'ny fizarana Satria ny antontan-taratasy sy ny antontan-taratasy dia afaka ahitana token maro kokoa noho ny mifanaraka amin'ny toe-javatra, dia manangona ny famantarana avy amin'ny Token avy amin'ny andian-tsarimihetsika azo jerena. Ny tsirairay dia mampifangaro ny sekoly tsirairay eo amin'ny sehatra (ohatra Atari, MassiveWeb, sns.), miaraka amin'ny fanamafisana ny tahirin-kevitra lehibe kokoa sy avo lenta kokoa (jereo ny Tabilao) Ao amin'ny fizarana Ho an'ny antsipirihany izany). L Ny C. L 1 3 2.4 Ny fametrahana Ny fametrahana Kato ho toy ny politika dia aseho ao amin'ny sary Voalohany, ny fangatahana, toy ny fampisehoana, dia tokenized, mamorona ny famaritana voalohany. Amin'ny fe-potoana, dia mandray ny token 1024 voalohany amin'ny fampisehoana. Avy eo ny tontolo iainana dia mamokatra ny fanadihadiana voalohany izay tokenized ary voasoratra amin'ny famaritana. Gato dia mamerina ny hetsika vektor autoregressively token iray amin'ny fotoana iray. Rehefa voasoratra ny token rehetra izay ahitana ny hetsika vektor (amin'ny famaritana ny hetsika amin'ny tontolo iainana), ny hetsika dia voasoratra amin'ny alalan'ny famerenana ny tokenization dingana voalaza ao amin'ny fizarana Ity hetsika ity dia alefa any amin'ny tontolo iainana izay dingana ary mahatonga ny fanandramana vaovao. Ny dingana dia mamerina. Ny modely dia mijery foana ny fanandramana sy ny hetsika teo aloha rehetra ao amin'ny varavarankely 1024 token. Nisy tombontsoa tamin'ny fampiasana ny fametrahana transformator XL nandritra ny fampiharana, na dia tsy nampiasaina nandritra ny fampiofanana aza. 3. 2.1 Ny (Dai et al., Ny taona 2019. 3 Ny tahirin-kevitra Gato dia nianatra amin'ny angon-drakitra maro izay ahitana traikefa amin'ny mpiara-miasa ao amin'ny tontolo ara-toekarena sy amin'ny tontolo tena izy, ary koa amin'ny angon-drakitra isan-karazany amin'ny fiteny voajanahary sy sary. Ny isan'ny token ho an'ny fanaraha-maso angon-drakitra dia voafantina amin'ny alàlan'ny hetsika tokenization voalaza ao amin'ny fizarana 1. 2.1 Ny 3.1 Ny asa fanaraha-maso simulated Ny asa fanaraha-maso dia ahitana angon-drakitra nateraky ny manam-pahaizana SoTA na manodidina ny SoTA fampiofanana mpiara-mianatra nianatra amin'ny tontolo iainana samihafa. Ny tontolo iainana simulated dia ahitana Meta-World (Y nampidirina amin'ny fampianarana meta-fanamafisana sy fampianarana maro, Sokoban Voatendry ho toy ny olana amin'ny fandaminana, BabyAI ho an'ny fampianarana amin'ny fiteny manaraka ao amin'ny grid-worlds, ny DM Control Suite (T ho an'ny fanaraha-maso tsy tapaka, ary koa ny DM Lab natao mba hampianatra ny mpitantana navigation sy ny 3D fahitana avy amin'ny Pixels voajanahary amin'ny fomba fijery ego-centric. miaraka amin'ny lalao Atari klasika (mampiasa lalao roa isika izay antsoina hoe ALE Atari sy ALE Atari Extended, jereo ny fizarana Ho an'ny antsipirihany izany). Ary ny al. Ny taona 2020 (Ny mpitsabo sy ny mpiara-miasa). Ny taona 2017 (Ny mpitarika sy ny mpiara-miasa) Ny taona 2018 Nandray anjara tamin'ny fanatanterahana sy ny fampandrosoana. Ny taona 2020 (Ny Tompo sy ny mpiara-miasa) Ny taona 2016 (Ary amin'ny alàlan'ny fahasamihafana, Ny taona 2013) Ny 1 Izahay koa dia ahitana ny Procgen Benchmark Ny modely RL Ankoatra izany dia ahitana asa efatra ampiasaina amin'ny fitaovam-piadiana Kinova Jaco avy amin'ny DM Manipulation Playground. Ny fizarana dia ahitana famaritana bebe kokoa momba ireo asa fanaraha-maso ireo, miaraka amin'ny mpampiasa RL izay nampiasaina mba hiteraka ny angon-drakitra. (Ny Tompo sy ny olon-drehetra) Ny taona 2020 (Ny Tompo sy ny olon-drehetra) Ny taona 2020. Ny Tompo sy ny olon-drehetra. Amin'ny taona 2021. F Fantatsika fa mahomby ny fampiofanana amin'ny andian-dahatsoratra voasoratra miaraka amin'ny famoahana farafaharatsiny 80% amin'ny fivoaran'ny manam-pahaizana ho an'ny asa. Ny fivoaran'ny manam-pahaizana dia mamaritra ny vokatra ambony maharitra izay azonao atao amin'ny manam-pahaizana. Aiza Ity dia ny isan'ny andiana voaangona ho an'ny asa, dia ny habetsaky ny varavarankely, ary Izany dia ny fiverenana ho an'ny episode Mba hahazoana tahirin-kevitra marina isika, amin'ny fomba fanao, dia mametraka tokony ho 10% amin'ny habetsaky ny angon-drakitra, na farafahakeliny 1000 andiana (izany hoe = min (1000) * 0 * 1 Ary ny N W Ny Ria i W W Ny 3.2 Ny fomba fijery sy ny fiteny Ny Gato dia nianatra amin'ny MassiveText fanangonana rakitra an-tsoratra lehibe amin'ny teny Anglisy avy amin'ny loharanom-baovao maro: tranonkala, boky, lahatsoratra vaovao ary code. (Ary amin'ny alàlan'ny Ny taona 2021, Nandray anjara tamin'ny fampiofanana an'i Gato ihany koa ny angon-drakitra maromaro amin'ny fiteny fijery. ALIGN Ny LTIP (Long Text & Image Pairs) dia ahitana sary 312 tapitrisa miaraka amin'ny soratra. Ny Ny fomba fisainana Ary ny Coco Captions Ny Ny dataset MultiModal MassiveWeb (M3W) dia mifanaraka amin'ny dataset miaraka amin'ny mpamorona sary sy lahatsoratra 3.3M sy 120k. Ny dia ahitana pejy 43M izay nosoratan'ny lahatsoratra sy ny sary. Nisy ihany koa ny angon-drakitra mifandraika amin'ny fanontaniana. indrindra OKVQA Ary ny VQAv2 amin'ny 9K sy 443K triplets amin'ny sary, fanontaniana sy valiny. Mba hanangana dingana fampiofanana avy amin'ireny, dia mandinika dimy (image, lahatsoratra) mpivady, tokenize azy ireo, fifangaroan-kevitra, ary avy eo dia pad na randomly mivezivezy amin'ny faharetan'ny fampiofanana ilaina. (Jia et al., Ny taona 2021 (Ary ny ankamaroan'ny mpiara-miasa amin'ny Ny taona 2022. (Ary amin'ny alàlan'ny fahasamihafana, Ny taona 2018 (Ny Tompo sy ny olon-drehetra) Ny taona 2015 (Ary ny alàlan'ny Ny taona 2022. Ny Marina Ary ny al. Ny taona 2019 (Ny Tompo sy ny olon-drehetra) Ny taona 2015 3.3 Robotics - RGB Stacking Benchmark (real and sim) Amin'ny alalan'ny fanandramana ny angon-drakitra ho an'ny hetsika ara-batana ao amin'ny tontolo tena izy, dia nifidy ny tontolo iainana robotic block stacking izay navoakan'i [Lee et al.](#_bookmark89) [(2021).](#_bookmark89) Ny tontolo iainana dia ahitana ny vavahadin'ny Robot Sawyer miaraka amin'ny 3-DoF cartesian speed control, DoF fanampiny ho an'ny haingana, ary fanafihana tsy miankina. Ny sehatry ny asa amin'ny Robot dia ahitana blôk plastika telo mainty, maitso sy mainty miaraka amin'ny endrika samihafa. Ny fanandramana azo jerena dia ahitana sary 128 128 avy amin'ny kamera, ny vavahadin' Amin'ny Skill Generalization, ho an'ny famolavolana sy ny tena zava-misy, dia mampiasa angon-drakitra avy amin'ny mpampiasa tsara indrindra sim2real avy amin'ny Nandray ny angon-drakitra izahay rehefa mifandray amin'ny RGB-stacking nomena (Izany dia mitovy amin'ny 387k trajectories mahomby amin'ny simulation sy 15k trajectories amin'ny tena). amin'ny simulation sy avy amin'ny tsara indrindra sim2real politika amin'ny tena robot (mihoatra ny 219k trajectories amin'ny ankapobeny). Tsarovy fa ity angon-drakitra ity dia voatahiry ho an'ny manokana Skill Mastery fanandramana ao amin'ny fizarana Ny Tompo sy ny Al. Ny taona 2021. Ny fitaovana fampiofanana Ny Tompo sy ny Al. Ny taona 2021. Ny 5.4 4 Capabilities of the generalist agent Ao amin'ity fizarana ity, dia manangona ny vokatra nataon'i Gato rehefa nianatra tamin'ny angon-drakitra voalaza etsy ambony. Izany dia midika fa ny vokatra rehetra amin'ny asa rehetra dia avy amin'ny modely tokana miaraka amin'ny fitsipika tokana. 5. 4.1 Ny asa fanaraha-maso simulated Figure shows the number of distinct control tasks for which Gato performs above a given score threshold, relative to expert performance demonstrated in Gato’s training data. 5 We report performance as a percentage, where 100% corresponds to the per-task expert and 0% to a random policy. For each simulated control task we trained our model on, we roll out the Gato policy on the corresponding environment 50 times and average the defined scores. As shown in Figure Gato performs over 450 out of 604 tasks at over a 50% expert score threshold. Ny 5 In ALE Atari Gato achieves the average human (or better) scores for 23 Atari games , nahatratra mihoatra ny roa heny amin'ny lalao 11. Raha mbola mihoatra noho ny Gato ny mpiara-miasa amin'ny aterineto RL izay niteraka ny angon-drakitra, dia azo alaina amin'ny fanampian'ny fahaiza-manao na amin'ny fampiasana fampiofanana RL an-tserasera fa tsy amin'ny fanaraha-maso fotsiny (jereo ny fizarana) izay manolotra manam-pahaizana iray ao amin'ny sehatra iray ALE Atari mpandray anjara izay mahomby noho ny olona amin'ny lalao 44). (Bellemare et al., 2013) 1 5.5 Ny zazakely Gato achieves over 80% of expert score for nearly all levels Ho an'ny asa sarotra indrindra, antsoina hoe BossLevel, Gato dia nahazo ny 75%. Ny roa hafa navoakan'ny baseline dia afaka mahita, BabyAI 1.0 sy BabyAI 1.1 , scored 77% and 90%, respectively, having trained on this single task alone using a million demonstrations. (Chevalier-Boisvert et al., Ny taona 2018 2 (Hui et al. 2020), On Meta-World (Y Gato achieves more than 50% for all 44 out of 45 tasks that we trained on, over 80% for 35 tasks, and over 90% for 3 tasks. On canonical DM Control Suite (T Gato achieves better than 50% of the expert score on 21 out of 30 tasks from state, and more than 80% for 18 tasks. u et al., 2020) assa et al., 2018), 4.2 Robotics First person teleoperation enables the collection of expert demonstrations. However, such demonstrations are slow and costly to collect. Data-efficient behavior cloning methods are therefore desirable for training a generalist robot manipulator and offline pretraining is thus a well-motivated area of research. To that end, we evaluated Gato on the established RGB Stacking benchmark for robotics. Skill Generalization Performance The Skill Generalization challenge from the RGB Stacking robotics benchmark tests the agent’s ability to stack objects of previously unseen shapes. The agent is trained on a dataset consisting of episodes of the robot stacking objects with a variety of different shapes. Five triplets of object shapes are, however, not included in the training data and serve as test triplets. We evaluated the trained generalist for 200 episodes per test triplet on the real robot. Table shows that our generalist agent’s success rate on each test triplet is comparable to the single task BC-IMP (filtered BC) baseline in 2 Lee et al. (2021). 4.3 Ny fomba fanehoan-kevitra The model demonstrates rudimentary dialogue and image captioning capabilities. Figure contains a rep-resentative sample of Gato’s image captioning performance. Figure mampiseho ohatra vitsivitsy avy amin'ny fifanakalozan-kevitra amin'ny lahatsoratra tsotra. 6 7 5 Analysis 5.1 Scaling Laws Analysis In Figure we analyze the aggregate in-distribution performance of the pretrained model as a function of the number of parameters in order to get insight into how performance could improve with increased model capacity. We evaluated 3 different model sizes (measured in parameter count): a 79M model, a 364M model, and a 1.18B model (Gato). We refer to Section for details on the three model architectures. 8, C Eto, ho an'ny habetsaky ny modely telo isika dia mandrafitra ny vokatra normalized araka ny fandrosoana ny fampiofanana. Mba hahazoana ity lanjany tokana ity, ho an'ny asa tsirairay dia mifanaraka amin'ny fampisehoana ny modely toy ny isan-jato amin'ny valin'ny manam-pahaizana (izay mitovy amin'ny ao amin'ny fizarana 1). Then for each domain listed in Table we average the percentage scores across all tasks for that domain. Finally, we mean-aggregate the percentage scores across all domains. We can see that for an equivalent token count, there is a significant performance improvement with increased scale. 4. 1 5.2 Out of distribution tasks In this section we want to answer the following question: Izany no antony nahatonga antsika handray ny angon-drakitra rehetra ho an'ny asa efatra avy amin'ny famolavolana alohan'ny fianarana: cartpole.swingup (domain'ny DM Control Suite), assembly-v2 (domain'ny Meta-World), order_of_apples_forage_simple (domain'ny DM Lab) ary boxing (domain'ny ALE Atari). Can our agent be used to solve a completely new task efficiently? Ideally, the agent could potentially learn to adapt to a new task via conditioning on a prompt including demonstrations of desired behaviour. However, due to accelerator memory constraints and the extremely long sequence lengths of tokenized demonstrations, the maximum context length possible does not allow the agent to attend over an informative-enough context. Therefore, to adapt the agent to new tasks or behaviours, we choose to fine-tune the agent’s parameters on a limited number of demonstrations of a single task, and then evaluate the fine-tuned model’s performance in the environment. Fine-tuning is very similar to pretraining with minor changes, such as different learning rate schedule; see Section for details. E We want to measure how choice of data used during pretraining influences post-fine-tuning performance. To this end, we compare Gato (trained on ) to variants trained on ablated datasets: all data 1. A model pretrained only on data from the same domain as the task to be fine-tuned on, . Domain ihany ny angon-drakitra 2. A model pretrained only on non-control data, . no control data 3. A model fine-tuned from scratch, i.e. no pretraining at all, . scratch Considering as all these experiments require training a new model from scratch and then also fine-tuning, we present results using the less compute-intensive 364M parameter architecture described in Section Results are shown in Figure Ny 5.1 9. Fine-tuning performance on both cartpole.swingup and assembly-v2 tasks, both of which do not require image processing, present similar trends. Pretraining on all the datasets yields the best results, followed by pretraining on the same domain only. This difference is smaller for assembly-v2 but consistent for all few shot datasets. For these non-image-based environments, we see either no benefit (cartpole.swingup) or even negative transfer (assembly-v2) when pretraining on Datasets, izay ahitana sary sy lahatsoratra ihany. Tsy misy fanaraha-maso Results for DM Lab order_of_apples_forage_simple are slightly different. Pretraining on DM Lab data only is already enough to approach the maximum reward of 19 and hence there is no observable benefit of adding data from different environments. What is different when compared to previously analysed no-vision environments is that pretraining on data helps, which can be possibly explained by the fact that agents in the DM Lab environment are fed images which, despite being simulated, are natural looking. Therefore, transfer from image captioning or visual grounded question answering tasks is possible. no control Tsy afaka mahita tombontsoa avy amin'ny fampiofanana mialoha amin'ny bokotra isika. Ny modely voasokafana dia toa miasa tsara kokoa noho ny iray amin'ireo karazana fampiofanana mialoha izay heverinay. Mihevitra isika fa izany no mahatonga izany satria ny sary fidirana ao amin'ny lalao dia tena samihafa amin'ny angon-drakitra hafa, izay midika fa sarotra ny famindrana. 5.3 Fine-tuning on Robotic Stacking Tasks Section manaporofo fa ny Gato fototra dia afaka manatanteraka asa maro samihafa amin'ny fomba fifaninanana amin'ny RGB Stacking Skill Generalization benchmark. Ao amin'ity fizarana ity, tiantsika ny mamaly ny fanontaniana manaraka: *Ahoana no manatsara ny asa robotika ny mpiara-miasa amin'ny mpiara-miasa amin'ny robotika rehefa ahafahana manatsara amin'ny fomba mitovy amin'ny fomba manatsara amin'ny asa vaovao ao amin'ny fizarana *We consider different model sizes and analyse the impact of pretraining datasets on the Skill Generalization benchmark, as well as a novel out of distribution task. Further analysis of fine-tuning with dataset ablations is in Appendix 4.2 5.2? I. Skill Generalization First, we would like to show that fine-tuning on object-specific data, similarly to what was done by Noho izany, dia nametraka ny Gato manokana izahay amin'ny andian-dahatsoratra dimy amin'ny fampisehoana avy amin'ny dataset. Each subset was obtained by random partitioning of a test dataset consisting of demonstrations gathered by a generalist sim-to-real agent stacking real test objects. We consider this setting, which is comparable to the fine-tuning baselines on RGB stacking tasks from and use the 5k dataset that their behavior cloning 5k results are obtained with. To best match their experiments, we change our return filtering scheme during training: instead of using only successful stacks, we condition on the normalized return of the episode. Lee et al. (2022), Ny fitsapana (Lee et al., 2022); Figure compares the success rate of Gato across different fine-tuning data regimes to the sim-to-real expert and a Critic-Regularized Regression (CRR) agent trained on 35k episodes of all test triplets. Gato, in both reality and simulation (red curves on the left and right figure, respectively), recovers the expert’s performance with only 10 episodes, and peaks at 100 or 1000 episodes of fine-tuning data, where it exceeds the expert. After this point (at 5000), performance degrades slightly but does not drop far below the expert’s performance. 10 (Wang et al., 2020) Fine-tuning and Model Size To better understand the benefit of large models for few-shot adaptation in robotics domains, we conducted an ablation on model parameter size. This section focuses on in-simulation evaluation. Figure compares the full 1.18B parameter Gato with the smaller 364M and 79M parameter variants for varying amounts of fine-tuning data. Although the 364M model overfits on one episode, causing performance to drop, there is a clear trend towards better adaptation with fewer episodes as the number of parameters is scaled up. The 79M model performs clearly worse than its bigger counterparts. The results suggest that the model’s greater capacity allows the model to use representations learned from the diverse training data at test time. 10 Adaptation to Perceptual Variations While the Skill Generalization task is an effective benchmark for motor Skill Generalization to shape varia-tions, it does not test the agent’s ability to adapt to perceptual variations and permutations in the objective specification. To further evaluate Gato’s generalization capabilities, we devised a new task in the RGB stacking benchmark where the goal is to stack the blue object on the green object, for test triplet 1 (see Figure First, we used a 3D mouse to collect 500 demonstrations of this task on the real robot, for a total of 2 hours and 45 minutes of demonstration data, and fine-tuned Gato on these episodes. Notably, all of the simulated and real robotics data in the pretraining set shows the robot successfully stacking the red object on the blue object, and the data does not include the object shapes in the test set. We found that additionally adding simulated demonstrations of the stack blue on green task to the fine-tuning dataset improved performance, and 10% was an ideal sampling ratio for this data. Ary ny 11). We achieved a final 60% success rate after evaluating fine-tuned Gato on the real robot, while a BC baseline trained from scratch on the blue-on-green data achieved only 0.5% success (1/200 episodes). Qualitatively, the BC baseline would consistently move towards the blue object and occasionally pick it up and place it on top of the green object, but a full, stable stack was almost never achieved. 5.4 Robotics: Skill Mastery Similarly to the Skill Generalization challenge discussed in Section the Skill Mastery challenge consists in training a robotic arm to stack blocks of different shapes. However, the Skill Mastery allows the agent to train on data involving the object shapes used for evaluation, i.e. the set in Skill Generalization becomes a part of the Skill Mastery set. Thus, this challenge serves to measure Gato’s performance on in-distribution tasks (possibly with initial conditions not seen in the training demonstrations). Our Skill Mastery results use an earlier version of the Gato architecture described in Appendix with no fine-tuning. 4.2, test training Ny Table compares the group-wise success percentage and the average success across object groups for Gato and the established BC-IMP baseline. Gato exceeds or closely matches BC-IMP’s performance on all but one training triplet. 3 5.5 Specialist single-domain multi-task agents In this section we show results obtained with two specialist (rather than generalist) agents. Both of them were trained on data from a single domain only and rolled out 500 times for each training task without any per-task fine-tuning. Meta-World The first agent uses the smallest architecture introduced in Section i.e. 79M parameters, and is trained on all 50 Meta-World tasks. While Gato has access to the state of the MuJoCo physics engine and unlimited task seeds, the agent presented here has no access to any extra features or tasks and uses the canonical API as in (Y This experiment is to show that the architecture proposed in our paper can be used to obtain state-of-the-art agents also at small scale. The training procedure was to train single-task MPO experts on each of the MT-50 tasks individually, recording the trajectories produced while training. This experience is then combined, or distilled, into a single agent, which achieves 96.6% success rate averaged over all 50 tasks. To the best of our knowledge this agent is the first one to accomplish nearly 100% average success rate simultaneously (multi-task) for this benchmark. See Table in the supplementary material (Section for the full list of tasks and corresponding success rates of our agent. 5.1, u et al., 2020). (Amin'ny ankapobeny izy ireo sy ny hafa) 2018) 7 K) ALE Atari We also trained a specialist agent on all 51 ALE Atari tasks. As the Atari domain is much more challenging than Meta-World, we used the Gato architecture with 1.18B parameters. The resulting agent performs better than the average human for 44 games (see Section for details on our evaluation and scoring). We want to note that the performance of online experts used to generate training data for the other 7 games were also below the average human. Hence, the specialist Atari agent achieved better than human performance for all games where data contained super-human episodes. 4.1 The specialist Atari agent outperforms our generalist agent Gato, which achieved super-human performance on 23 games. It suggests that scaling Gato may result in even better performance. We, however, purposely restricted Gato’s size such that it can be run in real-time on the real robot. 5.6 Attention Analysis We rendered the transformer attention weights over the image observations for various tasks, to gain a qualitative sense of how Gato attends to different regions of the image across tasks (see Figure Further details and visualizations for more tasks can be found in Appendix These visualizations clearly show that attention tracks the task-relevant objects and regions. 12). J. 5.7 Embedding Visualization Mba hahatakatra ny fomba fiasan'i Gato ny fampahalalana isan-karazany amin'ny asa, dia nanamafy ny fametrahana isan-karazany izahay. We analysed 11 tasks. For each task, we randomly sample 100 episodes and tokenize each of them. Then, from each episode we take a subsequence of 128 tokens, compute their embeddings (at layer 12, which is half the total depth of the transformer layers) and average them over the sequence. The averaged embeddings for all tasks are used as input to PCA, which reduces their dimensionality to 50. Then, T-SNE is used to get the final 2D embeddings. Figure shows the final T-SNE embeddings plotted in 2D, colorized by task. Embeddings from the same tasks are clearly clustered together, and task clusters from the same domain and modality are also located close to each other. Even held-out task (cartpole.swingup) is clustered correctly and lays next to another task from DM Control Suite Pixels. 13 6 Related Work The most closely related architectures to that of Gato are Decision Transformers , and Trajectory Transformer which showed the usefulness of highly generic LM-like architectures for a variety of control problems. Gato also uses an LM-like architecture for control, but with design differences chosen to support multi-modality, multi-embodiment, large scale and general purpose deployment. Pix2Seq also uses an LM-based architecture for object detection. Perceiver IO ., uses a transformer-derived architecture specialized for very long sequences, to model any modality as a sequence of bytes. This and similar architectures could be used to expand the range of modalities supported by future generalist models. (Chen et al., 2021b; Reid et al., 2022; Zheng et al., 2022; Ny fahalalahana et al. Ny taona 2021 (Janner et al., 2021), (Chen et al., 2022) (Jaegle et al 2021) Gato was inspired by works such as GPT-3 and Gopher mampitombo ny fetran'ny modely fiteny generalista; ary farany dia ny Flamingo generalist visual language model. developed the 540B parameter Pathways Language Model (PalM) explicitly as a generalist few-shot learner for hundreds of text tasks. (Brown et al., 2020) (Rae et al., 2021), (Alayrac et al., 2022) Chowdhery et al. (2022) Future work should consider how to unify these text capabilities into one fully generalist agent that can also act in real time in the real world, in diverse environments and embodiments. Gato also takes inspiration from recent works on multi-embodiment continuous control. used message passing graph networks to build a single locomotor controller for many simulated 2D walker variants. showed that transformers can outperform graph based approaches for incom-patible (i.e. varying embodiment) control, despite not encoding any morphological inductive biases. learn a modular policy for multi-task and multi-robot transfer in simulated 2D manipulation environments. train a universal policy conditioned on a vector representation of robot hardware, showing successful transfer both to simulated held out robot arms, and to a real world sawyer robot arm. Huang et al. Ny taona 2019 Ny Tompo sy ny Al. (2020) Devin et al. Ny taona 2017 Chen et al. (2018) A variety of earlier generalist models have been developed that, like Gato, operate across highly distinct domains and modalities. NPI trained a single LSTM mba hanatanterahana fandaharana isan-karazany toy ny famaritana isan-karazany sy ny fanampian'ny isan-karazany roa, mba hahafahan'ny tambajotra hampivelatra amin'ny tranga lehibe kokoa noho ireo hita nandritra ny fampiofanana. developed the MultiModel that trains jointly on 8 distinct speech, image and text processing tasks including classifica-tion, image captioning and translation. Modality-specific encoders were used to process text, images, audio and categorical data, while the rest of the network parameters are shared across tasks. proposed “ ”, describing a method for the incremental training of an increasingly general problem solver. manolotra modely fiteny maromaro azo fehezina izay azo alefa araka ny sehatra fiteny, subdomain, entity, fifandraisana eo amin'ny entity, daty, ary hetsika manokana fitondran-tena. (Reed & De Freitas, 2016) (Hochreiter & Schmidhuber, 1997) Kaiser et al. (2017) Schmidhuber (2018) one big net for everything Keskar et al. (2019) In this discussion, it is important to distinguish between one single multi-task network architecture versus one single neural network with the same weights for all tasks. Several poplar RL agents achieve good multi-task RL results within single domains such as Atari57 and DMLab However, it is much more common to use the same policy architecture and hyper-parameters across tasks, but the policy parameters are different in each task Toy izany koa amin'ny fomba RL maoderina ampiasaina amin'ny lalao board. Moreover, this choice has been adopted by off-line RL benchmarks and recent works on large sequence neural networks for control, including decision transformers and the Trajectory Transformer of In contrast, in this work we learn a single network with the same weights across a diverse set of tasks. (Ary amin'ny alàlan'ny fahasamihafana, Ny taona 2018; Song et al., 2020; Hessel et al., 2019). (Mnih et al., 2015; Tassa et al., 2018). (Schrittwieser et al., 2020). (Gulcehre et al., 2020; Fu et al., 2020) (Chen et al., 2021b; Reid et al., 2022; Zheng et al., 2022) Janner et al. (2021). Recent position papers advocate for highly generalist models, notably proposing one big net for everything, and on foundation models. However, to our knowledge there has not yet been reported a single generalist trained on hundreds of vision, language and control tasks using modern transformer networks at scale. Schmidhuber (2018) Bommasani et al. (2021) “Single-brain”-style models have interesting connections to neuroscience. famously stated that “ ”. Mountcastle found that columns of neurons in the cortex behave similarly whether associated with vision, hearing or motor control. This has motivated arguments that we may only need one algorithm or model to build intelligence Mountcastle (1978) the processing function of neocortical modules is qualitatively similar in all neocortical regions. Put shortly, there is nothing intrinsically motor about the motor cortex, nor sensory about the sensory cortex (Hawkins & Blakeslee, 2004). Sensory substitution provides another argument for a single model For example, it is possible to build tactile visual aids for blind people as follows. The signal captured by a camera can be sent via an electrode array on the tongue to the brain. The visual cortex learns to process and interpret these tactile signals, endowing the person with some form of “vision”. Suggesting that, no matter the type of input signal, the same network can process it to useful effect. (Bach-y Rita & Kercel, Ny taona 2003. Our work is based on deep autoregressive models, which have a long history and can be found in generative models of text, images, video and audio. Combining autoregressive generation with transformers (V has been of enormous impact in language modelling protein folding vision-language models (T code generation dialogue systems with retrieval capabilities speech recognition neural machine translation and more , Recently researchers have explored task decomposition and grounding with language models aswani et al., 2017; Devlin et al., 2018) (Brown et al., 2020; Rae et al., 2021), (Jumper et al., 2021), simpoukelli et al., 2021; Wang et al., 2021; Alayrac et al., 2022), (Chen et al., 2021c; Li et al., 2022b), (Ary amin'ny alàlan'ny 2021; Thoppilan et al., 2022), (Pratap Ary ny al. 2020), (Johnson et al., 2019) (Bommasani et al. 2021). (Huang et al., 2022; Ny et al., 2022). construct a control architecture, consisting of a sequence tokenizer, a pretrained language model and a task-specific feed-forward network. They apply it to VirtualHome and BabyAI tasks, and find that the inclusion of the pretrained language model improves generalisation to novel tasks. Similarly, demonstrate that vision models pretrained with self-supervised learning, especially crop segmentations and momentum contrast can be effectively incorporated into control policies. Li et al. (2022a) Parisi et al. (2022) (He et al., 2020), As mentioned earlier, transfer in Atari is challenging. researched transfer between ran-domly selected Atari games. They found that Atari is a difficult domain for transfer because of pronounced differences in the visuals, controls and strategy among the different games. Further difficulties that arise when applying behaviour cloning to video games like Atari are discussed by Rusu et al. (2016) Kanervisto et al. (2020). Efa nisy tombontsoa lehibe tamin'ny robotika mifototra amin'ny angon-drakitra. However, note that in robotics “ Ankoatra izany, isaky ny manatsara ny fitaovana ao amin'ny laboratoara robotika, dia mila manangona angon-drakitra vaovao isika ary manampy amin'ny fampiofanana indray.Mitakianay fa izany no antony ilaintsika ny manam-pahaizana izay afaka mifanohitra amin'ny fampiharana vaovao ary mianatra asa vaovao amin'ny angon-drakitra vitsivitsy. (Cabi et al., 2019; Ny Tompo sy ny hafa. 2021a). Bommasani et al. (2021) the key stumbling block is collecting the right data. Unlike language and vision data, robotics data is neither plentiful nor representative of a sufficiently diverse array of embodiments, tasks, and environments Generating actions using an autoregressive model can lead to causal “self-delusion” biases when there are confounding variables Ohatra, ny asany amin'ny sampana dia afaka mamantatra ny modely mba hamaha ny asa tsy marina rehefa manana asa maromaro mifandray amin'ny famaritana sy ny asa famaritana. we use prompt engineering in ambiguous tasks, conditioning our model on a successful demon-stration. This screens off confounding variables, reducing self-delusions. Another solution which we did not explore in this work is to use counterfactual teaching, where we train a model online using instantaneous expert feedback. We leave this for future investigation. (Ortega et al., 2021). 2, 7 Broader Impact Although generalist agents are still only an emerging area of research, their potential impact on society calls for a thorough interdisciplinary analysis of their risks and benefits. For the sake of transparency, we document the intended use cases of Gato in the model card in Appendix However, the tools for mitigating harms of generalist agents are relatively underdeveloped, and require further research before these agents are deployed. A. Since our generalist agent can act as a vision-language model, it inherits similar concerns as discussed in In addition, generalist agents can take actions in the the physical world; posing new challenges that may require novel mitigation strategies. For example, physical embodiment could lead to users anthropomorphizing the agent, leading to misplaced trust in the case of a malfunctioning system, or be exploitable by bad actors. Additionally, while cross-domain knowledge transfer is often a goal in ML research, it could create unexpected and undesired outcomes if certain behaviors (e.g. arcade game fighting) are transferred to the wrong context. The ethics and safety considerations of knowledge transfer may require substantial new research as generalist systems advance. (Wei-dinger et al., 2021; Bommasani et al., 2021; Rae et al., 2021; Alayrac et al., 2022). Technical AGI safety Mety ho sarotra kokoa ihany koa ny mandinika ireo mpampiasa manam-pahaizana izay miasa amin'ny fampiharana maro. Noho izany, ny fampianarana ny safidy, ny modely tsy azo antoka ary ny fanamafisana ny lanjany (R are especially important for the design of human-compatible generalist agents. It may be possible to extend some of the value alignment approaches for language to generalist agents. However, even as technical solutions are developed for value alignment, generalist systems could still have negative societal impacts even with the intervention of well-intentioned designers, due to unforeseen circumstances or limited oversight This limitation underscores the need for a careful design and a deployment process that incorporates multiple disciplines and viewpoints. (Bostrom, 2017) ussell, 2019) (Ouyang et al., 2022; Kenton et al., 2021) (Amodei et al., 2016). Ny fahatakarana ny fomba fiasan'ny modely ny fampahalalana, ary ny fahaiza-manao mety hitranga, dia mitaky fanandramana lehibe. has been shown to improve both interpretability and performance, and hence should be consid-ered in future designs of generalist agents. (Borgeaud et al., 2021; Menick et al., 2022; Nakano et al., 2021; Thoppilan et al., 2022) Although still at the proof-of-concept stage, the recent progress in generalist models suggests that safety researchers, ethicists, and most importantly, the general public, should consider their risks and benefits. We are not currently deploying Gato to any users, and so anticipate no immediate societal impact. However, given their potential impact, generalist models should be developed thoughtfully and deployed in a way that promotes the health and vitality of humanity. 8 Limitations and Future work 8.1 RL data collection Gato dia fomba fiasa mifototra amin'ny angon-drakitra, satria avy amin'ny fampianarana imitation. Raha ny fiteny voajanahary na sary dataset dia mora kokoa ny mahazo avy amin'ny tranonkala, dia tsy misy dataset amin'ny habetsahan'ny tranonkala ho an'ny asa fanaraha-maso amin'izao fotoana izao. That being said, there has already been extensive investigation into this issue. Offline RL aims at leveraging existing control datasets, and its increasing popularity has already resulted in the availability of more diverse and larger datasets. Richer environments and simulations are being built (e.g. Metaverse), and increasing numbers of users already interact with them among thousands of already deployed online games (e.g. there exists a large dataset of Starcraft 2 games). Real-life data has also been already stored for ML research purposes; for example, data for training self-driving cars is acquired from recording human driver data. Finally, while Gato uses data consisting of both observations and corresponding actions, the possibility of using large scale observation-only data to enhance agents has been already studied (Baker et al., 2022). Misaotra amin'ny famoahana lahatsary an-tserasera sy ny famoahana sehatra toy ny Youtube sy Twitch, ny angon-drakitra amin'ny fijerena ihany dia tsy sarotra kokoa ny manangona noho ny angon-drakitra amin'ny fiteny voajanahary, izay manoro hevitra ny fikarohana amin'ny hoavy mba hanatsarana ny Gato mba hianatra amin'ny angon-drakitra an-tserasera. While the previous paragraph focuses on alleviating drawbacks of data collection from RL agents, it is important to note that this approach presents a different set of tradeoffs compared to scraping web data and can be actually more practical in some situations. Once the simulation is set up and near SOTA agent trained, it can be used to generate massive amounts of high quality data. That is in contrast to the quality of web data which is notorious for its low quality. In short, we believe that acquiring suitable data is another research question on its own, and this is an active area of research with growing momentum and importance. 8.2 Prompt and short context Gato is prompted with an expert demonstration, which aids the agent to output actions corresponding to the given task. This is particularly useful since there is otherwise no task identifier available to the agent (that is in contrast to many multi-task RL settings). Gato infers the relevant task from the observations and actions in the prompt. However, the context length of our agent is limited to 1024 tokens which translates to the agent sometimes attending to only a few environment timesteps in total. This is especially the case for environments with image observations, where depending on the resolution each observation can result in more than one hundred tokens each. Hence for certain environments only a short chunk of a demonstration episode fits in the transformer memory. Due to this limited prompt context, preliminary experiments with different prompt structures resulted in very similar performance. Similarly, early evaluations of the model using prompt-based in-context learning on new environments did not show a significant performance improvement compared to prompt-less evaluation in the same setting. Noho izany, ny faharetan'ny kontekst dia voafetra amin'izao fotoana izao amin'ny fanorenana antsika, indrindra noho ny fanapahan-kevitry ny fahatsiarovana ny tenany. Maro ny fanorenana novolavolaina vao haingana dia mamela fifandraisana maharitra amin'ny fahombiazan'ny fahombiazana ary ireo fanavaozana ireo dia mety hanatsara ny fahombiazan'ny mpiara-miasa. Manantena isika fa handinika ireo fanorenana ireo amin'ny asa ho avy. 9 Conclusions Transformer sequence models are effective as multi-task multi-embodiment policies, including for real-world text, vision and robotics tasks. They show promise as well in few-shot out-of-distribution task learning. In the future, such models could be used as a default starting point via prompting or fine-tuning to learn new behaviors, rather than training from scratch. Given scaling law trends, the performance across all tasks including dialogue will increase with scale in parameters, data and compute. Better hardware and network architectures will allow training bigger models while maintaining real-time robot control capability. By scaling up and iterating on this same basic approach, we can build a useful general-purpose agent. Acknowledgments We would like to thank Dan Horgan, Manuel Kroiss, Mantas Pajarskas, and Thibault Sottiaux for their help with data storage infrastructure; Jean-Baptiste Lespiau and Fan Yang for help on concurrent evalua-tion; Joel Veness for advising on the model design; Koray Kavukcuoglu for helping inspire the project and facilitating feedback; Tom Erez for advising on the agent design and task selection for continuous control; Igor Babuschkin for helping code the initial prototype; Jack Rae for advising on the transformer language model codebase; Thomas Lampe for building robot infrastructure and advising on real robotics experiments; Boxi Wu for input on ethics and safety considerations; Pedro A. Ortega for advice in regard to causality and self-delusion biases. Author Contributions developed the project concept, wrote the initial prototype, and led the project overall. led architecture development for vision and text, built infrastructure for tokenization and prompting, and contributed heavily to overall agent development and evaluation. Scott Reed Konrad Żołna led work on optimizing the transformer architecture, ran the largest number of experi-ments, and analyzed scaling law properties and in-distribution agent performance. Emilio Parisotto was the technical lead, responsible for creating a scalable data loader and evaluator supporting hundreds of tasks at once, and for the initial robot integration with Gato. Sergio Gómez Colmenarejo developed the model including the sampler for the initial prototype, carried out ex-periments focusing on robotics, and created visualizations. Alexander Novikov built scalable storage infrastructure to provide Gato with SoTA-level agent expe-rience in Atari and other domains. Gabriel Barth-Maron conducted large scale agent data collection, built substantial data loading infrastructure, and integrated large scale visual-language datasets into the training of Gato. Mai Giménez contributed broadly to the Gato codebase including a bespoke distributed training sequence loader, and led the development of benchmarks for out-of-distribution generalization, and the training of competitive baseline agents. Yury Sulsky supported physical robotics infrastructure, conducted numerous evaluations and experiments to analyze the generalization properties of Gato, and contemplated broader ethical impact. Jackie Kay guided Gato’s deployment to the physical robot, provided strong existing base-lines for block stacking, and advised on model development and experimental design. Jost Tobias Springenberg developed the Gato dialogue and image captioning demonstrations, allowing users to easily probe the vision and language capacities of agents in development. Tom Eccles contributed to agent design as well as control datasets and environments with randomized physics and morphology variations. Jake Bruce Nandray anjara tamin'ny fanadihadiana ny fomba fijerin'ny fomba fijery. Ali Razavi contributed to the first prototype of Gato that worked on Atari, in addition to exploring alternative network architectures and training objectives. Ashley Edwards advised on agent design, experiment design and task selection, especially for continuous control applications. Nicolas Heess advised on model design and experiments, and provided feedback in regular meetings. Yutian Chen advised on the design and planning of robotics efforts. Raia Hadsell advised on all aspects of the project, especially model architecture, training strategies and benchmark design. Oriol Vinyals Izy no mpitantana tetikasa voalohany; manolotra tanjona fototra, manara-maso ny fandrosoana, manampy amin'ny fanapahan-kevitra sy ny valin-teny, ary manaraha-maso ny fandaharam-potoana. 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B Agent Data Tokenization Details In this section we provide additional details on our tokenization schemes. Our agent data is sequenced as follows: • are presented to the agent in order of time (timesteps). Episodes • Amin'ny ankapobeny dia aseho amin'ny fehezan-dalàna manaraka: Timesteps ([ 1: 1: 1: ]) are ordered lexicographically by key, each item is sequenced as follows: – Observations y k, x m, z n ∗ Text tokens ( Ny 1 : ) are in the same order as the raw input text. y k ∗ Image patch tokens ( 1: Misy ao amin'ny raharaham-barotra izy ireo. x m ∗ Tensors ( 1: ) (such as discrete and continuous observations) are in row-major order. z n – (' [Na: Fa inona no fanantenan'ny mpihatsaravelatsihy, na dia mahazo harena be aza. Separator | – ( 1: ) are tokenized as discrete or continuous values and in row-major order. Actions a A A full sequence of tokens is thus given as the concatenation of data from T timesteps: where L = T(k + m + n + 1 + A) is the total number of tokens. Ny singa tsirairay amin'ireo tensors ao amin'ny famaritana famantarana dia mu-law companded toy ny ao amin'ny WaveNet. (Oord et al., 2016): with parameters µ = 100 and M = 256. (If the floating-point tensor is in the action set, we do not need to compand the elements in the sequence because actions are only defined in the range \[ 1, 1\] for all our environments.) All the elements are subsequently clipped so that they fall in the set \[ 1, 1\]. Finally, they are discretized using bins of uniform width on the domain \[ 1,1\]. We use 1024 bins and shift the resulting integers so they are not overlapping with the ones used for text tokens. The tokenized result is therefore a sequence of integers within the range of \[32000, 33024). See Figure and Figure for visualizations of tokenizing and sequencing values (both discrete and con-tinuous) and images. See Section for details about local position encodings referenced in the figures. 14 15 C C Model Architecture C.1 Ny Hyperparameter Transformer The transformer hyperparameters of Gato are presented in Table We also list the hyperparameters of smaller architecture variants used in Section 5. 5. C.2 Embedding Function The ResNet block uses the v2 architecture contains GroupNorm with 32 groups instead of LayerNorm and GELU activation functions instead of RELU. The block is diagrammed in Figure (He et al., 2016b), (Wu & He, 2018) (Ary amin'ny alàlan'ny 2016), (Hendrycks & Gimpel, 2016) 16. C.3 Ny fametrahana ny toerana After tokens are mapped into token embeddings, two position encodings are added to the token embeddings (when applicable) to provide temporal and spatial information to the model. These are described below. Patch Position Encodings These position encodings convey information about a patch’s global position within the image from which the patch was extracted. First, the relative row and column intervals of the patch are calculated by normalizing the patch’s pixel intervals by the image resolution. The row and column normalized intervals are then quantized into a vocabulary size (we use 128) and are used to index a row and column table of learnable position encodings. The method in which the quantized row and column intervals are converted into indices depends on whether we are training or evaluating the model: during training a random index is uniformly sampled from the quantized interval, while during evaluation we deterministically take the (rounded) mean of the interval. Once row and column position encoding are retrieved from the embedding table, they are added onto the token embedding produced by the resnet embedding function, as described previously. Mba hampisehoana mazava kokoa ity dingana ity, dia manome ohatra ao amin'ny endrika [17.] (#_bookmark144) Manaraka ny dingana amin'ny patch voasokitra amin'ny mena eo amin'ny ankavia ny endrika. Ny sary dia amin'ny fahafahana 80 64 ary ny patch tsirairay dia 16 16, izay midika fa misy 5 4 = 20 patches manontolo. Ny patch voasokitra dia manomboka amin'ny pixel row interval \[16*,* 32\] ary pixel column interval \[32*,* 64\]. Normalized, noho izany, ny row interval dia \[0*25*,* 0*.*5\] ary ny fe-potoana dia \[0*.*4*, * 0*.*6\]. Avy eo isika dia mizara isan-karazany ny intervals Local Observation Position Encodings The local observation position encoding adds positional information about where observation tokens are positioned within the local time-step they were an element of. First, we reiterate that, during tokenization, for each time-step all elements of the observation set are tokenized into sequences and concatenated into an observation sequence. Each token in this observation sequence is given an index which corresponds to the sequence order, i.e. the first token is 0 and the last is the length of the observation sequence minus one. After embedding, for any tokens that were a part of an observation set, the corresponding observation token index is used to index an embedding table of learnable position encodings, with one embedding for every possible observation token index (in practice we simply set the table size to a large value like 512). / Ny fametrahana ny toerana dia ampidirina amin'ny fametrahana ny token fanadihadiana mba hiteraka ny fametrahana token farany. Tsarovy fa ny token rehetra dia omena ny fametrahana ny toerana mitovy, na inona na inona ny toerana ao amin'ny fametrahana ny dingana fotoana. 18. D Ny fampiofanana mialoha Ho an'ny modely rehetra dia mampiasa ny AdamW Ny fanamafisana ara-dalàna dia maharitra mandritra ny 15*,* 000 dingana, manomboka amin'ny tahan'ny fampianarana 1e-7 ary mifarana amin'ny tahan'ny fampianarana ambony samihafa miankina amin'ny modely (jereo ny Tabilao) This learning rate is then cosine decayed by a factor 10x over 1,000,000 steps. The AdamW optimizer has parameters 1 = 0*. 2 = 0.*95 and = 1e-8. We use a batch size of 512 and a sequence length of 1024 tokens for all models. Optimizer: (Loshchilov & Hutter, 2017) 6). β 9, β ϵ We train with an AdamW weight decay parameter of 0.1. Additionally, we use stochastic depth nandritra ny fampiofanana mialoha, izay tsirairay amin'ireo transformer sub-layers (toy ny tsirairay Multi-Head Attention sy Dense Feedforward layer) dia voavaha amin'ny mety ho 0.1. Regularization: (Huang et al., 2016) E Fine-tuning Setup For all models we use the Adam optimizer with a constant learning rate of 1e-5. The Adam optimizer has parameters 1 = 0*. 2 = 0.*95 and = 1e-8. We use a batch size of 64 and a sequence length of 1024 tokens for all models. We train for 10,000 gradient steps. Optimizer: (Kingma & Ba, 2014) β 9, β ϵ We use dropout with a rate of 0.1. Regularization: (Ary amin'ny alàlan'ny fahasamihafana, 2014) Izahay mandinika ny mpiasa isaky ny 100 dingana fampianarana. Ny fanombanana tsirairay dia manambara ny eo ho eo amin'ny 10 mihazakazaka amin'ny valin'ny fanaraha-maso. Ny eo ho eo amin'ny 5 toy izany valin'ny noforonina (ny hanangona 50 mihazakazaka miaraka). Ny farany fanamafisana vokatra dia voafaritra ho ny ambony indrindra amin'ireo valin'ny. Evaluation: Nampiasa ny angon-drakitra rehetra ho an'ny fanatanterahana fanatanterahana izahay, fa tsy nampiasa ny angon-drakitra rehetra ho an'ny fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatanterahana fanatan as a separate point. Datasets: 5.2 We have not altered any of the tasks and used their canonical versions. As 3 out of 4 tasks are open sourced, they do not need further explanation. For the fourth task, DMLab order_of_apples_forage_simple, the goal is to collect apples in the right order, green ones first followed by the gold one. Task settings: F Data Collection Details F.1 Atari Ny voalohany (izay antsointsika hoe ALE Atari) dia ahitana lalao 51 canonical avy amin'ny Arcade Learning Environment The second (that we refer to as ALE Atari Extended) is a set of alternative games miaraka amin'ny fomba lalao sy ny fahasamihafana voatendry amin'ny alàlan'ny fampisehoana tsirairay. (Bellemare et al., 2013). 3 Ho an'ny tontolo iainana tsirairay ao amin'ireo vondrona ireo dia manangona angon-drakitra amin'ny alalan'ny fampiofanana ny Muesli agent for 200M total environment steps. We record approximately 20,000 random episodes generated by the agent during training. (Ny Tompo sy ny mpiara-miasa aminy) Ny taona 2021 Ny F2 dia Sokoban Sokoban is a planning problem in which the agent has to push boxes to target locations. Some of the moves are irreversible and consequently mistakes can render the puzzle unsolvable. Planning ahead of time is therefore necessary to succeed at this puzzle. We use a Muesli agent to collect training data. (Ny mpitsabo sy ny mpiara-miasa). 2017), (Hessel et al., 2021) F.3 BabyAI BabyAI is a gridworld environment whose levels consist of instruction-following tasks that are described by a synthetic language. We generate data for these levels with the built-in BabyAI bot. The bot has access to extra information which is used to execute optimal solutions, see Section C in the appendix of ho an'ny antsipiriany bebe kokoa momba ny bot. dia manangona 100,000 andiana ho an'ny dingana tsirairay. (Chevalier-Boisvert et al., Ny taona 2018 F.4 DeepMind Control Suite The DeepMind Control Suite (T Ny is a set of physics-based simulation environments. For each task in the control suite we collect two disjoint sets of data, one using only state features and another using only pixels. We use a D4PG mpampiasa mba hanangona angon-drakitra avy amin'ny asa miaraka amin'ny toe-javatra, ary ny MPO based agent to collect data using pixels. unyasuvunakool et al Ny taona 2020 Ny Tompo sy ny hafa. 2018) (Barth-Maron et al., 2018) (Abdolmaleki et al., 2018) We also collect data for randomized versions of the control suite tasks with a D4PG agent. These versions randomize the actuator gear, joint range, stiffness, and damping, and geom size and density. There are two difficulty settings for the randomized versions. The small setting scales values by a random number sampled from the union of intervals [0*. ,* 0*. [Ny iray amin'ireo ,* 1*. . ,* 0*. [1. ,* 1*.*4]. 9 95] ∪ 05 1]. The large setting scales values by a random number sampled from the union of intervals [0 6 8] ∪ 2 F.5 DeepMind Lab Ny laboratoara dia , ny olona voalohany 3D tontolo iainana natao mba hampianatra ny mpampianatra 3D fahitana avy amin'ny Pixel Raw entana amin'ny ego-centric fomba fijery, navigation, ary ny fandaminana. (Beattie et al. Ny taona 2016 Nandray anjara tamin'ny fampiofanana agent jointly on a set of 18 parent DM Lab levels that generate maps procedurally for each new episode. Data was collected by executing the agent on these 18 levels, as well as an additional set of 237 levels handcrafted to test a diverse set of skills. (Espeholt et al., Ny taona 2018 Ny ambaratonga 18 dia miavaka amin'ny fahasamihafana goavana amin'ny sarin'ny soratra. Ny fahasamihafana eo amin'ny ambaratonga dia mifototra amin'ny hyper-parametrika ampiasaina amin'ny dingana famokarana. Ireo hyper-parametrika ireo dia mifehy ny endri-javatra avo lenta toy ny karazana fanorenana voajanahary, ny fahasamihafana amin'ny fiteny fametrahana, na ny fisian'ny fitaovana manokana. Ny ambaratonga voajanahary dia novolavolaina mba hanatsarana ny fampisehoana ny RL mpiara-miasa nianatra an-tserasera amin'izy ireo. Raha oharina amin'ny ambaratonga ray aman-dreny, ny tsirairay amin'ireo ambaratonga 237 fanampiny dia mampiasa ny sarintany mitovy, ary ny fahasamihafana lehibe eo amin'ny toeran'ny sarintany amin'ny ambaratonga mitovy dia ny tsiambaratelo toy ny loko ny rindrina na ny fahazavana toe-javatra. voajanahary sy natao hanandrana isan-karazany ny fahaiza-manaon'ny toy ny mandeha amin'ny tendrombohitra na mampiasa fitaovana manokana. izy ireo dia mitovy amin'ireo ambaratonga hita ao amin'ny endrika 3, endrika 7 sy endrika 8 ao amin'ny taratasy voalaza etsy ambony Tsy Betsy sy ny hafa. (2016). Additional information on the 18 parent levels (and their relation to the other levels) is presnted in details in the NeurIPS Workshop talk by Daniel Tanis . Metodika ho an'ny fikarohana momba ny tontolo iainana RL 4 In total, we collected data for 255 levels from the DeepMind Lab (18 parent levels and 237 handcrafted levels), 254 of which were used while training Gato. The remaining level was used for out of distribution evaluation. F6 ny Procgen Benchmark Procgen Ity fitaovana ity dia fitaovana ahitana tontolo iainana mitovy amin'ny Atari 16 izay novolavolaina mba handrefesana ny fahombiazan'ny sampana sy ny fanitarana amin'ny fampianarana fanamafisana. agent on each of the environments. We used the hard difficulty setting for all environments except for maze and heist, which we set to easy. (Ny Tompo sy ny olon-drehetra) 2020) (Ary amin'ny ankapobeny dia tsy misy dikany). Ny taona 2018 F.7 Modular ny RL Ny modely RL dia ny fanangonana ny MuJoCo (T based continuous control environments, composed of three sets of variants of the OpenAI Gym Walker2d-v2, Humanoid-v2, and Hopper-v2. Each variant is a morphological modification of the original body: the set of morphologies is generated by enumerating all possible subsets of limbs, and keeping only those sets that a) contain the torso, and b) still form a connected graph. This results in a set of variants with different input and output sizes, as well as different dynamics than the original morphologies. We collected data by training a single morphology-specific D4PG agent on each variant for a total of 140M actor steps, this was done for 30 random seeds per variant. (Ny Tompo sy ny olon-drehetra) 2020) Mifandraisa amin'ny alàlan'ny Ny taona 2012 (Ny Tompo sy ny mpiara-miasa aminy) Ny taona 2016 F.8 DeepMind Manipulation Lalao fanatanjahantena Ny DeepMind Manipulation Playground amin'ny alalan'ny Izahay dia manangona angon-drakitra ho an'ny asa Jaco 4 (box, stack banana, insertion, ary slide) amin'ny fampiasana ny Critic-Regularized Regression (CRR) agent Ny angon-drakitra voaangona dia ahitana ny toe-javatra ara-batana ao amin'ny MuJoCo, izay ampiasaintsika amin'ny fampiofanana sy ny fanombanana ny Gato. (Ary amin'ny ankapobeny izy ireo) Ny taona 2021 (Ny Tompo sy ny olon-drehetra) 2020) F.9 Meta-World Ny tontolo iainana (Y is a suite of environments for benchmarking meta-reinforcement learning and multi-task learning. We collect data from all train and test tasks in the MT50 mode by training a MPO agent miaraka amin'ny toetra tsy voafetra momba ny tontolo iainana ary amin'ny fidirana amin'ny toe-javatra ao amin'ny milina fizika MuJoCo. Ny angon-drakitra voaangona dia ahitana koa ny toe-javatra ao amin'ny milina fizika MuJoCo. Ary ny al. Ny taona 2020 5 (Amin'ny ankapobeny izy ireo sy ny hafa) Ny taona 2018 G Real robotics fanombanana antsipirihany In the real world, control is asynchronous; physics does not wait for computations to finish. Thus, inference latency is a concern for evaluating a large model for real world tasks. In robotics, a fast control rate is thought to be critical for reacting to dynamic phenomena. The robot setup for RGB stacking has a 20Hz control rate (0.05 second timestep) by design. In order to reach an acceptable margin of latency, we modified inference at evaluation time by shortening the context length to 1. We also implemented a parallel sampling scheme where all the action tokens are zeroed out in the input sequences during training so we can sample all tokens corresponding to a robot action in a single model inference step instead of autoregressively as it’s done in other domains. We found that the 1.18B parameter model was able to run on the hardware accelerators in our robots (NVidia GeForce RTX 3090s), but still overran the 20Hz control rate by a small amount (~0.01 seconds). We use the sparse reward function described in ho an'ny fanodinana ny angon-drakitra. Hisafidy trajectories fotsiny izahay amin'ny task success; that is, a sparse reward of 1 on the final timestep. Ny Tompo sy ny Al. (2021) Ny farany H Skill Mastery architecture Ny isa voalaza ho an'ny Skill Mastery benchmark dia novolavolaina amin'ny fandefasana modely zero-shot izay mampiasa dikan-tenan'ny Gato. Ankoatra ny resnet patch embedding, dia nampiasaina ny rafitra mitovy amin'ny fampiasana transformer eo an-toerana mba hampifangaroan'ny sary patch token. Ny toerana misy ny toerana sy ny toerana misy ny patch dia tsy ampiasaina. Ireo fanovana ireo dia novolavolaina ary hita fa hanatsarana ny fahombiazan'i Gato taorian'ny fanovana ny angon-drakitra mialoha ny fampiofanana (satria nanapa-kevitra izahay ny hifantoka amin'ny Skill Generalization fa tsy ny fanamby Skill Mastery), izany no antony mahatonga azy ireo ho an'ny rafitra I Robotika fanampiny ablation Nanatanteraka serivisy ny ablation ao amin'ny simulation mba hahatakatra tsara kokoa ny fiantraikany amin'ny angon-drakitra pre-training isan-karazany ao amin'ny sehatry ny robotics (jereo sary) Nampiditra ny fotodrafitrasa mitovy amin'ny ao amin'ny fizarana Ny DM Control-only agent dia mihoatra noho ny base Gato amin'ny zero-shot famindrana sy amin'ny be dia be ny fanamafisana angon-drakitra, midika fa Gato dia mety tsy mampiasa ny fampisehoana nianatra avy amin'ny dataset mifototra amin'ny lahatsoratra rehefa mifanaraka amin'ny asa robotics. Ny ankamaroan'ny sehatra ihany ny manam-pahaizana dia manao ny tsara indrindra amin'ny ankapobeny, mifanaraka amin'ny CRR fototra amin'ny 1 fanamafisana episode ary mifanaraka amin'ny angon-drakitra bebe kokoa, midika fa Gato amin'ny habetsaky ny ankehitriny dia afaka mampiasa ny fahaiza-manaon'ny fanamafisana ho an'ny angon-drakitra mahomby sy mahomby Ny faha 19). 5.2, J Momba ny fanaraha-maso Ho an'ny famerenana ny fifantohana amin'ny endriky ny fifantohana, dia nahazo ny logits fifantohana, ny tensor amin'ny habetsahan'ny ( ) where is the number of heads and ny isan'ny token ao amin'ny andian-dahatsoratra. ny ( )th entry of this matrix can be interpreted as the amount that head Miandry amin'ny token from token Noho ny fandaharam-potoan'i Gato, misy token maromaro isaky ny fotoana. Noho izany, mba hanolotra ny fahatakarana ho an'ny fotoana iray manokana, dia naka ny sub-matrix izay mifanaraka amin'izany fotoana izany. Avy eo dia nampiasa softmax teo amin'ny andian-dahatsoratra ity matrix ity izahay mba hanamafisana ny lanjany. Satria liana fotsiny izahay amin'ny fanehoan-kevitra amin'ny token taloha, dia nanala ny diagonal amin'ny fametrahana azy amin'ny tsy fahampian'ny tsy fahampian'ny alàlan'ny softmax. Ny T, ny T H T h, i, j h j i To measure the importance of each patch, we averaged the attention weights over the corresponding column. Because Gato uses a causal transformer, the attention matrix is lower triangular, so the mean was only considered over the sub-column below the diagonal of the matrix. This corresponds to the average attention paid to particular patch over a whole timestep. Amin'ny fampiasana ity fomba ity, dia hita fa ny sarin'ny fifantohana ao amin'ny sarin'ny voalohany ny transformera no tena azo esorina, mifanaraka amin'ny famaritana ny Ny lohateny sasany dia manara-maso mazava ny fikambanana sy ny faritra manokana amin'ny asa ao amin'ny sary. mampiseho ny fanehoan-kevitra ho an'ny lohateny voafidy amin'ny tanana ao amin'ny lafiny voalohany ho an'ny asa maro. Ny fiantraikan'i Abnar sy Zuidema Amin'ny taona 2021. 20 K Ny vokatra amin'ny antsipiriany ho an'ny manam-pahaizana Meta-World Ny manam-pahaizana Meta-World manam-pahaizana voalaza ao amin'ny fizarana mahatratra 96.6% ny tahan'ny fahombiazana eo ho eo amin'ny asa 50 Meta-World. Ny tahan'ny fahombiazana amin'ny antsipirihany dia naseho ao amin'ny Tabilao Nanamarika ny mpiara-miasa 500 indray izahay ho an'ny asa tsirairay. 5.5 7. L Per-domain vokatra ho an'ny Cat Izahay dia mamaritra ny fampisehoana ny Gato ho an'ny asa fanaraha-maso simulated ao amin'ny fizarana In Table we present normalized per-domain results. We evaluated agent 50 times for each task. Ny 4.1 8, Ity lahatsoratra ity dia azo jerena ao amin'ny archiv amin'ny lisansa CC by 4.0 Deed (Attribution 4.0 International). Ity lahatsoratra ity dia azo jerena ao amin'ny archiv amin'ny lisansa CC by 4.0 Deed (Attribution 4.0 International).
