智能科学与技术学报 ›› 2022, Vol. 4 ›› Issue (3): 308-323.doi: 10.11959/j.issn.2096-6652.202235
赵超1,2, 许杰1,2, 陈星宇1,2, 梅魁志1,2, 兰旭光1,2
修回日期:
2022-06-30
出版日期:
2022-09-15
发布日期:
2022-09-01
作者简介:
赵超(1996- ),男,西安交通大学人工智能与机器人研究所博士生,主要研究方向为机器人持续学习基金资助:
Chao ZHAO1,2, Jie XU1,2, Xingyu CHEN1,2, Kuizhi MEI1,2, Xuguang LAN1,2
Revised:
2022-06-30
Online:
2022-09-15
Published:
2022-09-01
Supported by:
摘要:
当前机器人技术面临的较大限制之一是难以适应不断变化的任务,当机器人面对新环境或者学习新任务时,会不可避免地遗忘旧环境或旧任务的经验。为了总结机器人持续学习的研究和发展现状,首先介绍了持续学习的框架和评价基准,然后阐述了持续学习在机器人任务中的必要性和面临的挑战,并对持续学习的发展现状进行了回顾,最后展望了机器人持续学习的发展前景,提出了一些有价值的研究问题。
中图分类号:
赵超, 许杰, 陈星宇, 等. 机器人持续学习进展与展望[J]. 智能科学与技术学报, 2022, 4(3): 308-323.
Chao ZHAO, Jie XU, Xingyu CHEN, et al. A review of continual learning for robotics[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 308-323.
[1] | RING M B . Continual learning in reinforcement environments[M]. Austin: University of Texas, 1994. |
[2] | TJOMSLAND J , KALKAN S , GUNES H . Mind your manners! A dataset and a continual learning approach for assessing social appropriateness of robot actions[J]. Frontiers in Robotics and AI, 2022,9:669420. |
[3] | GROSSMANN A . Continual learning for mobile robots[D]. Birmingham:University of Birmingham, 2001. |
[4] | NEHMZOW U , RIANO L . A proposal for continual learning in robotics[C]// Proceedings of Towards Autonomous Robotic Systems.[S.l.:s.n.], 2009. |
[5] | LIU B , XIAO X S , STONE P . A lifelong learning approach to mobile robot navigation[J]. IEEE Robotics and Automation Letters, 2021,6(2): 1090-1096. |
[6] | HADSELL R , RAO D , RUSU A A ,et al. Embracing change:continual learning in deep neural networks[J]. Trends in Cognitive Sciences, 2020,24(12): 1028-1040. |
[7] | PARISI G I , KEMKER R , PART J L ,et al. Continual lifelong learning with neural networks:a review[J]. Neural Networks, 2019,113: 54-71. |
[8] | DE LANGE M , ALJUNDI R , MASANA M ,et al. A continual learning survey:defying forgetting in classification tasks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022,44(7): 3366-3385. |
[9] | VERWIMP E , DE LANGE M , TUYTELAARS T . Rehearsal revealed:the limits and merits of revisiting samples in continual learning[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 9365-9374. |
[10] | LESORT T , LOMONACO V , STOIAN A ,et al. Continual learning for robotics:definition,framework,learning strategies,opportunities and challenges[J]. Information Fusion, 2020,58: 52-68. |
[11] | LIU Y Y , SCHIELE B , SUN Q R . Adaptive aggregation networks for class-incremental learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 2544-2553. |
[12] | ZHAI M Y , CHEN L , HE J W ,et al. Piggyback GAN:efficient lifelong learning for image conditioned generation[M]// Computer vision -ECCV 2020. Cham: Springer International Publishing, 2020: 397-413. |
[13] | MITCHELL T , COHEN W , HRUSCHKA E ,et al. Never-ending learning[J]. Communications of the ACM, 2018,61(5): 103-115. |
[14] | MCCLOSKEY M , COHEN N J . Catastrophic interference in connectionist networks:the sequential learning problem[J]. Psychology of Learning and Motivation, 1989,24: 109-165. |
[15] | TRAORé R , CASELLES-DUPRé H , LESORT T ,et al. Continual reinforcement learning deployed in real-life using policy distillation and Sim2Real transfer[C]// Proceedings of ICML Workshop on Multi-Task and Lifelong Reinforcement Learning.[S.l.:s.n.], 2019. |
[16] | ZHANG Y , YANG Q . A survey on multi-task learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. |
[17] | PAN S J , YANG Q . A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359. |
[18] | HOI S C H , SAHOO D , LU J ,et al. Online learning:a comprehensive survey[J]. Neurocomputing, 2021,459: 249-289. |
[19] | GOODFELLOW I J , MIRZA M , XIAO D ,et al. An empirical investigation of catastrophic forgetting in gradient-based neural networks[C]// Proceedings of the 2nd International Conference on Learning Representations.[S.l.:s.n.], 2014. |
[20] | LOPEZ-PAZ D , RANZATO M . Gradient episodic memory for continual learning[J]. arXiv preprint,2017,arXiv:1706.08840. |
[21] | REBUFFI S A , KOLESNIKOV A , SPERL G ,et al. iCaRL:incremental classifier and representation learning[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 5533-5542. |
[22] | SAENKO K , KULIS B , FRITZ M ,et al. Adapting visual category models to new domains[J]. Lecture Notes in Computer Science, 2010: 213-226. |
[23] | LECUN Y , BOTTOU L , BENGIO Y ,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11): 2278-2324. |
[24] | GANIN Y , LEMPITSKY V . Unsupervised domain adaptation by backpropagation[C]// Proceedings of the 32nd International Conference on Machine Learning.[S.l.:s.n.], 2015: 1180-1189. |
[25] | HULL J J . A database for handwritten text recognition research[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(5): 550-554. |
[26] | NETZER Y , WANG T . Reading digits in natural images with unsupervised feature learning[J]. Advances in Neural Information Processing Systems, 2011. |
[27] | BELLEMARE M G , NADDAF Y , VENESS J ,et al. The arcade learning environment:an evaluation platform for general agents[J]. Journal of Artificial Intelligence Research, 2013,47: 253-279. |
[28] | JUNG S , AHN H , CHA S ,et al. Continual learning with node-importance based adaptive group sparse regularization[J]. Advances in Neural Information Processing Systems, 2020,33: 3647-3658. |
[29] | RUSU A A , RABINOWITZ N C , DESJARDINS G ,et al. Progressive neural networks[J]. arXiv preprint,2016,arXiv:1606.04671. |
[30] | WANG L Y , ZHANG M T , JIA Z F ,et al. AFEC:active forgetting of negative transfer in continual learning[J]. arXiv preprint,2021,arXiv:2110.12187. |
[31] | MENDEZ J A , WANG B Y , EATON E . Lifelong policy gradient learning of factored policies for faster training without forgetting[J]. Advances in Neural Information Processing Systems, 2020. |
[32] | XU K , VERMA S , FINN C ,et al. Continual learning of control primitives:skill discovery via reset-games[J]. Advances in Neural Information Processing Systems, 2020. |
[33] | YANG F , YANG C , LIU H ,et al. Evaluations of the gap between supervised and reinforcement lifelong learning on robotic manipulation tasks[C]// Proceedings of the Conference on Robot Learning.[S.l.:s.n.], 2022: 547-556. |
[34] | TODOROV E , EREZ T , TASSA Y . MuJoCo:a physics engine for model-based control[C]// Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2012: 5026-5033. |
[35] | BROCKMAN G , CHEUNG V , PETTERSSON L ,et al. OpenAI Gym[J]. arXiv preprint,2016,arXiv:1606.01540. |
[36] | JULIAN R , SWANSON B , SUKHATME G S ,et al. Never stop learning:the effectiveness of fine-tuning in robotic reinforcement learning[J]. arXiv preprint,2020,arXiv:2004.10190. |
[37] | DíAZ-RODRíGUEZ N , LOMONACO V , FILLIAT D ,et al. Don’t forget,there is more than forgetting:new metrics for continual learning[J]. arXiv preprint,2018,arXiv:1810.13166. |
[38] | CHAUDHRY A , DOKANIA P K , AJANTHAN T ,et al. Riemannian walk for incremental learning:understanding forgetting and intransigence[C]// Proceedings of the European Conference on Computer Vision.[S.l.:s.n.], 2018: 532-547. |
[39] | YIN H , YANG P , LI P . Mitigating forgetting in online continual learning with neuron calibration[J]. Advances in Neural Information Processing Systems, 2021,34. |
[40] | WO?CZYK M , ZAJ?C M , PASCANU R ,et al. Continual world:a robotic benchmark for continual reinforcement learning[J]. Advances in Neural Information Processing Systems, 2021. |
[41] | JIANG Y , BHARADWAJ S , WU B ,et al. Temporal-logic-based reward shaping for continuing learning tasks[J]. arXiv preprint,2020,arXiv:2007.01498. |
[42] | KEMKER R , MCCLURE M , ABITINO A ,et al. Measuring catastrophic forgetting in neural networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018,32(1): 3390-3398. |
[43] | RIEMER M , CASES I , AJEMIAN R ,et al. Learning to learn without forgetting by maximizing transfer and minimizing interference[C]// Proceedings of the 7th International Conference on Learning Representations.[S.l.:s.n.], 2019. |
[44] | HENNING C , CERVERA M , D’ANGELO F , ,et al. Posterior meta-replay for continual learning[J]. Advances in Neural Information Processing Systems, 2021,34. |
[45] | DONG J , CONG Y , SUN G ,et al. I3DOL:Incremental 3D object learning without catastrophic forgetting[C]// Proceedings of the AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2021: 6066-6074. |
[46] | ZHOU M , XIAO J , CHANG Y F ,et al. Image de-raining via continual learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 4905-4914. |
[47] | CHERAGHIAN A , RAHMAN S , RAMASINGHE S ,et al. Synthesized feature based few-shot class-incremental learning on a mixture of subspaces[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 8641-8650. |
[48] | KUKLEVA A , KUEHNE H , SCHIELE B . Generalized and incremental few-shot learning by explicit learning and calibration without forgetting[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 9000-9009. |
[49] | LOO N , SWAROOP S , TURNER R E . generalized variational continual learning[C]// Proceedings of the International Conference on Learning Representations.[S.l.:s.n.], 2020. |
[50] | GORUR D . Linear mode connectivity in multitask and continual learning[J]. arXiv preprint,2020,arXiv:2010.04495. |
[51] | KIRKPATRICK J , PASCANU R , RABINOWITZ N ,et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017,114(13): 3521-3526. |
[52] | VERMA V K , LIANG K J , MEHTA N ,et al. Efficient feature transformations for discriminative and generative continual learning[J]. arXiv preprint,2021,arXiv:2103.13558. |
[53] | CHA S , HSU H , HWANG T ,et al. CPR:classifier-projection regularization for continual learning[C]// Proceedings of the International Conference on Learning Representations.[S.l.:s.n.], 2020. |
[54] | HINTON G , VINYALS O , DEAN J . Distilling the knowledge in a neural network[J]. arXiv preprint,2015,arXiv:1503.02531. |
[55] | ZHAI M Y , CHEN L , TUNG F ,et al. Lifelong GAN:continual learning for conditional image generation[C]// Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2019: 2759-2768. |
[56] | TAO X Y , HONG X P , CHANG X Y ,et al. Few-shot class-incremental learning[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2020: 12180-12189. |
[57] | MENG Q E , SHIN'ICHI S ,, . ADINet:attribute driven incremental network for retinal image classification[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2020: 4032-4041. |
[58] | FINI E , LATHUILIèRE S , SANGINETO E ,et al. Online continual learning under extreme memory constraints[J]. Lecture Notes in Computer Science, 2020: 720-735. |
[59] | SHMELKOV K , SCHMID C , ALAHARI K . Incremental learning of object detectors without catastrophic forgetting[C]// Proceedings of 2017 IEEE International Conference on Computer Vision.Piscataway:IEEE Press.[S.l.:s.n.], 2017: 3420-3429. |
[60] | SMITH J , HSU Y C , BALLOCH J ,et al. Always be dreaming:a new approach for data-free class-incremental learning[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 9354-9364. |
[61] | CASTRO F M , MARíN-JIMéNEZ M J , GUIL N ,et al. End-to-end incremental learning[J]. Lecture Notes in Computer Science, 2018: 241-257. |
[62] | KIM J Y , CHOI D W . Split-and-bridge:adaptable class incremental learning within a single neural network[J]. arXiv preprint,2021,arXiv:2107.01349. |
[63] | DENG D R , CHEN G Y , HAO J Y ,et al. Flattening sharpness for dynamic gradient projection memory benefits continual learning[J]. arXiv preprint,2021,arXiv:2110.04593. |
[64] | TANG S X , CHEN D P , ZHU J G ,et al. Layerwise optimization by gradient decomposition for continual learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 9629-9638. |
[65] | SAHA G , GARG I , ROY K . Gradient projection memory for continual learning[J]. arXiv preprint,2021,arXiv:2103.09762. |
[66] | JIN X S , DU J Y , REN X . Gradient based memory editing for task-free continual learning[J]. arXiv preprint,2020,arXiv:2006.15294. |
[67] | CAI Z P , SENER O , KOLTUN V . Online continual learning with natural distribution shifts:an empirical study with visual data[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 8261-8270. |
[68] | CHAUDHRY A , KHAN N , DOKANIA P K ,et al. Continual learning in low-rank orthogonal subspaces[J]. Advances in Neural Information Processing Systems, 2020: 1-12. |
[69] | LIN S , YANG L , FAN D L ,et al. TRGP:trust region gradient projection for continual learning[J]. arXiv preprint,2022,arXiv:2202.02931. |
[70] | ALJUNDI R , KELCHTERMANS K , TUYTELAARS T . Task-free continual learning[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 11246-11255. |
[71] | KIM C D , JEONG J , MOON S ,et al. Continual learning on noisy data streams via self-purified replay[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 517-527. |
[72] | CHA H , LEE J , SHIN J . Co2L:contrastive continual learning[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 9496-9505. |
[73] | KUZINA A , EGOROV E , BURNAEV E . BooVAE:boosting approach for continual learning of VAE[J]. Advances in Neural Information Processing Systems, 2021. |
[74] | MARACANI A , MICHIELI U , TOLDO M ,et al. RECALL:replay-based continual learning in semantic segmentation[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 7006-7015. |
[75] | AYUB A , WAGNER A R . EEC:learning to encode and regenerate images for continual learning[J]. arXiv preprint,2021,arXiv:2101.04904. |
[76] | ROSTAMI M , KOLOURI S , PILLY P ,et al. Generative continual concept learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 5545-5552. |
[77] | SHIN H , LEE J K , KIM J ,et al. Continual learning with deep generative replay[J]. arXiv preprint,2017,arXiv:1705.08690. |
[78] | OSTAPENKO O , PUSCAS M , KLEIN T ,et al. Learning to remember:a synaptic plasticity driven framework for continual learning[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 11313-11321. |
[79] | HU X T , TANG K H , MIAO C Y ,et al. Distilling causal effect of data in class-incremental learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 3956-3965. |
[80] | LI T J , KE Q H , RAHMANI H ,et al. Else-Net:elastic semantic network for continual action recognition from skeleton data[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 13414-13423. |
[81] | SINGH P , VERMA V K , MAZUMDER P ,et al. Calibrating CNNs for lifelong learning[J]. Advances in Neural Information Processing Systems, 2020. |
[82] | PELLEGRINI L , GRAFFIETI G , LOMONACO V ,et al. Latent replay for real-time continual learning[C]// Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2020: 10203-10209. |
[83] | KUNDU J N , VENKATESH R M , VENKAT N ,et al. Class-incremental domain adaptation[M]// Computer vision - ECCV 2020. Cham: Springer International Publishing, 2020: 53-69. |
[84] | WEI K , DENG C , YANG X ,et al. Incremental embedding learning via zero-shot translation[C]// Proceedings of the AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2021: 10254-10262. |
[85] | STAN S , ROSTAMI M . Unsupervised model adaptation for continual semantic segmentation[C]// Proceedings of the AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2021: 2593-2601. |
[86] | ZHU F , ZHANG X Y , WANG C ,et al. Prototype augmentation and self-supervision for incremental learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 5867-5876. |
[87] | DE LANGE M , TUYTELAARS T . Continual prototype evolution:learning online from non-stationary data streams[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2021: 8230-8239. |
[88] | OLIVERI G , VAN LAAKE L C , CARISSIMO C ,et al. Continuous learning of emergent behavior in robotic matter[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021,118(21): e2017015118. |
[89] | YAN S P , XIE J W , HE X M . DER:dynamically expandable representation for class incremental learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 3013-3022. |
[90] | VENIAT T , DENOYER L , RANZATO M . Efficient continual learning with modular networks and task-driven priors[C]// Proceedings of the 9th International Conference on Learning Representations.[S.l.:s.n.], 2021. |
[91] | XU J , MA J , GAO X ,et al. Adaptive progressive continual learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. |
[92] | CHEN T , ZHENYU Z , SIJIA L ,et al. Long live the lottery:the existence of winning tickets in lifelong learning[J]. International Conference on Learning Representations, 2021,51(4): 273-287. |
[93] | SHI Y J , YUAN L , CHEN Y P ,et al. Continual learning via bit-level information preserving[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2021: 16669-16678. |
[94] | MAZUMDER P , SINGH P , RAI P . Few-shot lifelong learning[J]. arXiv preprint,2021,arXiv:2103.00991. |
[95] | CACCIA M , RODRIGUEZ P , OSTAPENKO O ,et al. Online fast adaptation and knowledge accumulation:a new approach to continual learning[J]. Advances in Neural Information Processing Systems, 2020. |
[96] | HURTADO J , RAYMOND-SAEZ A , SOTO A . Optimizing reusable knowledge for continual learning via metalearning[J]. Advances in Neural Information Processing Systems, 2021. |
[97] | VON OSWALD J , ZHAO D , KOBAYASHI S ,et al. Learning where to learn:gradient sparsity in meta and continual learning[J]. arXiv preprint,2021,arXiv:2110.14402. |
[98] | GUPTA G , YADAV K , PAULL L . La-MAML:look-ahead meta learning for continual learning[C]// Proceedings of the Advances in Neural Information Processing Systems.[S.l.:s.n.], 2020. |
[99] | VOLPI R , LARLUS D , ROGEZ G . Continual adaptation of visual representations via domain randomization and meta-learning[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognitio. Piscataway:IEEE Press, 2021: 4441-4451. |
[100] | LUO Y D , HUANG Z , ZHANG Z ,et al. Learning from the past:continual meta-learning with Bayesian graph neural networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 5021-5028. |
[101] | ZHANG J , ZHANG J T , GHOSH S ,et al. Regularize,expand and compress:nonexpansive continual learning[C]// Proceedings of 2020 IEEE Winter Conference on Applications of Computer Vision. Piscataway:IEEE Press, 2020: 843-851. |
[102] | EBRAHIMI S , ELHOSEINY M , DARRELL T ,et al. Uncertain ty-guided continual learning with Bayesian neural networks[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019: 75-78. |
[103] | RAVAGLIA L , RUSCI M , NADALINI D ,et al. A TinyML platform for on-device continual learning with quantized latent replays[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2021,11(4): 789-802. |
[104] | PUTRA R V W , SHAFIQUE M . lpSpikeCon:enabling low-precision spiking neural network processing for efficient unsupervised continual learning on autonomous agents[J]. arXiv preprint,2022,arXiv:2205.12295. |
[105] | BURHAN HAFEZ M , WERMTER S . Behavior self-organization supports task inference for continual robot learning[C]// Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2021: 6739-6746. |
[106] | XU J , ZHU Z . Reinforced continual learning[J]. Advances in Neural Information Processing Systems, 2018: 899-908. |
[107] | LECARPENTIER E , ABEL D , ASADI K ,et al. Lipschitz lifelong reinforcement learning[C]// Proceedings of the AAAI Conference on Artificial Intelligenc.[S.l.:s.n.], 2021: 8270-8278. |
[108] | BRAFMAN R I , TENNENHOLTZ M . R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning[J]. IJCAI International Joint Conference on Artificial Intelligence, 2001,3: 953-958. |
[109] | ABEL D , JINNAI Y , YUE G ,et al. Policy and value transfer in lifelong reinforcement learning[C]// Proceedings of the 35th International Conference on Machine Learning.[S.l.:s.n.], 2018: 30-41. |
[110] | LOMONACO V , DESAI K R , CULURCIELLO E ,et al. Continual reinforcement learning in 3D non-stationary environments[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE Press, 2020: 999-1008. |
[111] | CHANDAK Y , THEOCHAROUS G , NOTA C ,et al. Lifelong learning with a changing action set[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 3373-3380. |
[112] | MNIH V , KAVUKCUOGLU K , SILVER D ,et al. Human-level control through deep reinforcement learning[J]. Nature, 2015,518(7540): 529-533. |
[113] | ROLNICK D , AHUJA A , SCHWARZ J ,et al. Experience replay for continual learning[J]. arXiv preprint,2019,arXiv:1811.11682. |
[114] | LU K , GROVER A , ABBEEL P ,et al. Reset-free lifelong Learning with skill-space planning[C]// Proceedings of the International Conference on Learning Representations.[S.l.:s.n.], 2020. |
[115] | ALJUNDI R . Continual learning in neural networks[J]. arXiv preprint,2019,arXiv:1910.02718. |
[116] | MORENO-MU?OZ P , RAMíREZ D , ARTéS-RODRíGUEZ A , . Continual learning for infinite hierarchical change-point detection[C]// Proceedings of 2020 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2020: 3582-3586. |
[117] | BING L . Learning on the job:online lifelong and continual learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(9): 13544-13549. |
[118] | FLESCH T , BALAGUER J , DEKKER R ,et al. Comparing continual task learning in minds and machines[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018,115(44): 10313-10322. |
[119] | BARRON H C . Neural inhibition for continual learning and memory[J]. Current Opinion in Neurobiology, 2021,67: 85-94. |
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