{"type":"ScholarlyArticle","title":"A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points","authors":[{"name":"Zihe Liu","slug":"zihe-liu","given":"Zihe","family":"Liu","affiliation":"Australian Artificial Intelligence Institute (AAII) , University of Technology Sydney"},{"name":"Jie Lu","slug":"jie-lu","given":"Jie","family":"Lu","affiliation":"Australian Artificial Intelligence Institute (AAII) , University of Technology Sydney"},{"name":"Guangquan Zhang","slug":"guangquan-zhang","given":"Guangquan","family":"Zhang","affiliation":"Australian Artificial Intelligence Institute (AAII) , University of Technology Sydney"},{"name":"Junyu Xuan","slug":"junyu-xuan","given":"Junyu","family":"Xuan","affiliation":"Australian Artificial Intelligence Institute (AAII) , University of Technology Sydney"}],"abstract":"Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, tracking continuous environmental changes and adapting to unpredictable conditions is challenging yet crucial because it ensures that systems remain reliable and flexible in practical scenarios. Our research introduces Behavior-Aware Detection and Adaptation (BADA), an innovative framework that merges environmental change detection with behavior adaptation. The key inspiration behind our method is that policies exhibit different global behaviors in changing environments. Specifically, environmental changes are identified by analyzing variations between behaviors using Wasserstein distances without manually set thresholds. The model adapts to the new environment through behavior regularization based on the extent of changes. The results of a series of experiments demonstrate better performance relative to several current algorithms. This research also indicates significant potential for tackling this long-standing challenge.","doi":"10.24963/ijcai.2024/512","url":"https://ijcai.org/proceedings/2024/512","pdf":"https://ijcai.org/proceedings/2024/0512.pdf","markdown":"https://ijcai.org/proceedings/2024/512.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/512","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":512,"pages":{"first":4634,"last":4642},"keywords":["Machine Learning: ML: Reinforcement learning"],"videos":[],"references":[{"position":1,"raw":"[Aljundi et al., 2017] Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars. Expert gate: Lifelong learning with a network of experts. In IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 3366-3375, 2017.","title":"Expert gate: Lifelong learning with a network of experts","authors":"Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bellot and van der Schaar, 2021] Alexis Bellot and Mihaela van der Schaar. A kernel two-sample test with selection bias. In Cassio de Campos and Marloes H. Maathuis, edi- tors, Proceedings of the Thirty-Seventh Conference on Un- certainty in Artificial Intelligence, volume 161 of Proceed- ings of Machine Learning Research, pages 205-214, 2021.","title":"Alexis Bellot and Mihaela van der Schaar. A kernel two-sample test with selection bias","authors":"Van Bellot, Der Schaar","year":"2021","venue":"Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bing et al., 2023] Zhenshan Bing, David Lerch, Kai Huang, and Alois C. Knoll. Meta-reinforcement learning in non- stationary and dynamic environments. IEEE Trans. Pat- tern Anal. Mach. Intell., 45(3):3476-3491, 2023.","title":"Meta-reinforcement learning in nonstationary and dynamic environments","authors":"Bing","year":"2023","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Da Silva et al., 2006] Bruno C Da Silva, Eduardo W Basso, Ana LC Bazzan, and Paulo M Engel. Dealing with non- stationary environments using context detection. In Inter- national conference on Machine learning (ICML), pages 217-224, 2006.","title":"Dealing with nonstationary environments using context detection","authors":"Bruno C Da Silva, Eduardo W Basso, Ana Lc Bazzan, Paulo M Engel","year":"2006","venue":"International conference on Machine learning (ICML)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Delarue et al., 2020] Arthur Delarue, Ross Anderson, and Christian Tjandraatmadja. Reinforcement learning with combinatorial actions: An application to vehicle rout- ing. Advances in Neural Information Processing Systems, 33:609-620, 2020.","title":"Reinforcement learning with combinatorial actions: An application to vehicle routing","authors":"Delarue","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[D'Eramo et al., 2020] Carlo D'Eramo, Davide Tateo, An- drea Bonarini, Marcello Restelli, Jan Peters, et al. Shar- ing knowledge in multi-task deep reinforcement learn- ing. In 8th International Conference on Learning Representations,{ICLR} 2020, Addis Ababa, Ethiopia, April 26-30, 2020, pages 1-11. OpenReview. net, 2020.","title":"Sharing knowledge in multi-task deep reinforcement learning","authors":"Carlo D' Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters","year":"2020","venue":"8th International Conference on Learning Representations,{ICLR} 2020","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fifty et al., 2021] Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn. Effi- ciently identifying task groupings for multi-task learn- ing. Advances in Neural Information Processing Systems, 34:27503-27516, 2021.","title":"Efficiently identifying task groupings for multi-task learning","authors":"Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Good, 2013] Phillip Good. Permutation tests: a practi- cal guide to resampling methods for testing hypotheses. Springer Science & Business Media, 2013.","title":"Permutation tests: a practical guide to resampling methods for testing hypotheses","authors":"Phillip Good","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hadoux et al., 2014] Emmanuel Hadoux, Aurélie Beynier, and Paul Weng. Sequential decision-making under non- stationary environments via sequential change-point de- tection. In Learning over Multiple Contexts (LMCE), 2014.","title":"Sequential decision-making under nonstationary environments via sequential change-point detection","authors":"Emmanuel Hadoux, Aurélie Beynier, Paul Weng","year":"2014","venue":"Learning over Multiple Contexts (LMCE)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hu et al., 2023] Mingzhe Hu, Jiahan Zhang, Luke Matkovic, Tian Liu, and Xiaofeng Yang. Reinforce- ment learning in medical image analysis: Concepts, applications, challenges, and future directions. Journal of Applied Clinical Medical Physics, 24(2):e13898, 2023.","title":"Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions","authors":"Hu","year":"2023","venue":"Journal of Applied Clinical Medical Physics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2022] Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, and Kun Zhang. Adarl: What, where, and how to adapt in transfer reinforcement learning. In International Conference on Learning Representations (ICLR), 2022.","title":"Adarl: What, where, and how to adapt in transfer reinforcement learning","authors":"Huang","year":"2022","venue":"International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kirkpatrick et al., 2017] James Kirkpatrick, Razvan Pas- canu, Neil Rabinowitz, Joel Veness, Guillaume Des- jardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcom- ing catastrophic forgetting in neural networks. Proceed- ings of the National Academy of Sciences, 114(13):3521- 3526, 2017.","title":"Overcoming catastrophic forgetting in neural networks","authors":"James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska","year":"2017","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2022] Shikun Liu, Stephen James, Andrew Davi- son, and Edward Johns. Auto-lambda: Disentangling dy- namic task relationships. Transactions on Machine Learn- ing Research, 2022.","title":"Auto-lambda: Disentangling dynamic task relationships","authors":"Shikun Liu, Stephen James, Andrew Davison, Edward Johns","year":"2022","venue":"Transactions on Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2024] Zihe Liu, Jie Lu, Junyu Xuan, and Guangquan Zhang. Deep reinforcement learning in non- stationary environments with unknown change points. IEEE Transactions on Cybernetics, 2024.","title":"Deep reinforcement learning in nonstationary environments with unknown change points","authors":"Liu","year":"2024","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lomonaco et al., 2020] Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, and Davide Maltoni. Continual re- inforcement learning in 3d non-stationary environments. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 248-249, 2020.","title":"Continual reinforcement learning in 3d non-stationary environments","authors":"Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lopez-Paz and Ranzato, 2017] David Lopez-Paz and Marc'Aurelio Ranzato. Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems (NIPS), pages 6467-6476, 2017.","title":"Gradient episodic memory for continual learning","authors":"David Lopez, - Paz, Marc'aurelio Ranzato","year":"2017","venue":"Advances in Neural Information Processing Systems (NIPS)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mosavi et al., 2020] Amirhosein Mosavi, Yaser Faghan, Pe- dram Ghamisi, Puhong Duan, Sina Faizollahzadeh Ard- abili, Ely Salwana, and Shahab S Band. Comprehensive review of deep reinforcement learning methods and appli- cations in economics. Mathematics, 8(10):1640, 2020.","title":"Comprehensive review of deep reinforcement learning methods and applications in economics","authors":"Mosavi","year":"2020","venue":"Mathematics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Oikonomou et al., 2023] Katerina Maria Oikonomou, Ioan- nis Kansizoglou, and Antonios Gasteratos. A hy- brid spiking neural network reinforcement learning agent for energy-efficient object manipulation. Machines, 11(2):162, 2023.","title":"A hybrid spiking neural network reinforcement learning agent for energy-efficient object manipulation","authors":"Maria Katerina, Ioannis Oikonomou, Antonios Kansizoglou, Gasteratos","year":"2023","venue":"Machines","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Olkin and Pukelsheim, 1982] Ingram Olkin and Friedrich Pukelsheim. The distance between two random vectors with given dispersion matrices. Linear Algebra and its Applications, 48:257-263, 1982.","title":"The distance between two random vectors with given dispersion matrices","authors":"Ingram Olkin, Friedrich Pukelsheim","year":"1982","venue":"Linear Algebra and its Applications","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pacchiano et al., 2020] Aldo Pacchiano, Jack Parker- Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, and Michael Jordan. Learning to score behaviors for guided policy optimization. In International Conference on Machine Learning, pages 7445-7454, 2020.","title":"Learning to score behaviors for guided policy optimization","authors":"Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Padakandla et al., 2020] Sindhu Padakandla, KJ Prabuchandran, and Shalabh Bhatnagar. Reinforce- ment learning algorithm for non-stationary environments. Applied Intelligence, 50(11):3590-3606, 2020.","title":"Reinforcement learning algorithm for non-stationary environments","authors":"Sindhu Padakandla, Shalabh Kj Prabuchandran, Bhatnagar","year":"2020","venue":"Applied Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Panaretos and Zemel, 2019] Victor M Panaretos and Yoav Zemel. Statistical aspects of wasserstein distances. Annual review of statistics and its application, 6:405-431, 2019.","title":"Statistical aspects of wasserstein distances","authors":"M Panaretos Victor, Yoav Zemel","year":"2019","venue":"Annual review of statistics and its application","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Prabuchandran et al., 2021] KJ Prabuchandran, Nitin Singh, Pankaj Dayama, Ashutosh Agarwal, and Vinayaka Pandit. Change point detection for compositional multivariate data. Applied Intelligence, pages 1-26, 2021.","title":"Change point detection for compositional multivariate data","authors":"Nitin Kj Prabuchandran, Pankaj Singh, Ashutosh Dayama, Vinayaka Agarwal, Pandit","year":"2021","venue":"Applied Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rakelly et al., 2019] Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen. Efficient off- policy meta-reinforcement learning via probabilistic con- text variables. In International conference on machine learning(ICML), pages 5331-5340, 2019.","title":"Efficient offpolicy meta-reinforcement learning via probabilistic context variables","authors":"Rakelly","year":"2019","venue":"International conference on machine learning(ICML)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sasaki and Yamashina, 2020] Fumihiro Sasaki and Ryota Yamashina. Behavioral cloning from noisy demonstra- tions. In International Conference on Learning Represen- tations, 2020.","title":"Behavioral cloning from noisy demonstrations","authors":"Yamashina Sasaki, Fumihiro Sasaki, Ryota Yamashina","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sasaki, 2007] Yutaka Sasaki. The truth of the f-measure. Teach tutor mater, 1(5):1-5, 2007.","title":"The truth of the f-measure","authors":"Yutaka Sasaki","year":"2007","venue":"Teach tutor mater","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Schulman et al., 2015] John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889-1897. PMLR, 2015.","title":"Trust region policy optimization","authors":"John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz","year":"2015","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Schulman et al., 2017] John Schulman, Filip Wolski, Pra- fulla Dhariwal, Alec Radford, and Oleg Klimov. Prox- imal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.","title":"Proximal policy optimization algorithms","authors":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Schwarz et al., 2018] Jonathan Schwarz, Wojciech Czar- necki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell. Progress & compress: A scalable framework for continual learning. In International Conference on Machine Learn- ing(ICML), pages 4528-4537, 2018.","title":"Progress & compress: A scalable framework for continual learning","authors":"Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell","year":"2018","venue":"International Conference on Machine Learning(ICML)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shin et al., 2017] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep gener- ative replay. In Advances in Neural Information Process- ing Systems(NIPS), pages 2990-2999, 2017.","title":"Continual learning with deep generative replay","authors":"Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim","year":"2017","venue":"Advances in Neural Information Processing Systems(NIPS)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Standley et al., 2020] Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Which tasks should be learned together in multi- task learning? In International Conference on Machine Learning, pages 9120-9132. PMLR, 2020.","title":"Which tasks should be learned together in multitask learning?","authors":"Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tiwari et al., 2023] Prayag Tiwari, Abdullah Lakhan, Rutvij H Jhaveri, and Tor-Morten Gronli. Consumer- centric internet of medical things for cyborg applications based on federated reinforcement learning. IEEE Transactions on Consumer Electronics, 2023.","title":"Consumercentric internet of medical things for cyborg applications based on federated reinforcement learning","authors":"Prayag Tiwari, Abdullah Lakhan, H Rutvij, Tor-Morten Jhaveri, Gronli","year":"2023","venue":"IEEE Transactions on Consumer Electronics","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Trabucco et al., 2022] Brandon Trabucco, Mariano Phielipp, and Glen Berseth. Anymorph: Learning transferable polices by inferring agent morphology. In International Conference on Machine Learning, pages 21677-21691.","title":"Anymorph: Learning transferable polices by inferring agent morphology","authors":"Brandon Trabucco, Mariano Phielipp, Glen Berseth","year":null,"venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"PMLR, 2022.","title":null,"authors":"Pmlr","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Van Borkulo et al., 2022] Claudia D Van Borkulo, Riet van Bork, Lynn Boschloo, Jolanda J Kossakowski, Pia Tio, Robert A Schoevers, Denny Borsboom, and Lourens J Waldorp. Comparing network structures on three aspects: A permutation test. Psychological methods, 2022.","title":"Comparing network structures on three aspects: A permutation test","authors":"Claudia D Van Borkulo, Riet Van Bork, Lynn Boschloo, Jolanda J Kossakowski, Pia Tio, Robert A Schoevers, Denny Borsboom, Lourens J Waldorp","year":"2022","venue":"Psychological methods","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Villani, 2009] Cédric Villani. Optimal transport: old and new, volume 338. Springer, 2009.","title":"Optimal transport: old and new","authors":"Villani","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Welch, 1990] William J Welch. Construction of permuta- tion tests. Journal of the American Statistical Association, 85(411):693-698, 1990.","title":"Construction of permutation tests","authors":"Welch William","year":"1990","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wydmuch et al., 2019] Marek Wydmuch, Michał Kempka, and Wojciech Jaśkowski. ViZDoom Competitions: Play- ing Doom from Pixels. IEEE Transactions on Games, 11(3):248-259, 2019. The 2022 IEEE Transactions on Games Outstanding Paper Award.","title":"ViZDoom Competitions: Playing Doom from Pixels","authors":"Marek Wydmuch, Michał Kempka, Wojciech Jaśkowski","year":"2019","venue":"IEEE Transactions on Games","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xie et al., 2021] Annie Xie, James Harrison, and Chelsea Finn. Deep reinforcement learning amidst continual struc- tured non-stationarity. In International Conference on Ma- chine Learning(ICML), pages 11393-11403, 2021.","title":"Deep reinforcement learning amidst continual structured non-stationarity","authors":"Annie Xie, James Harrison, Chelsea Finn","year":"2021","venue":"International Conference on Machine Learning(ICML)","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xu, 2019] Lihu Xu. Approximation of stable law in wasserstein-1 distance by stein's method. The Annals of Applied Probability, 29(1):458-504, 2019.","title":"Approximation of stable law in wasserstein-1 distance by stein's method","authors":"Lihu Xu, Xu","year":"2019","venue":"The Annals of Applied Probability","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu et al., 2020] Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In The Con- ference on Robot Learning (CoRL), pages 1094-1100, 2020.","title":"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning","authors":"Yu","year":"2020","venue":"The Conference on Robot Learning (CoRL)","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yu et al., 2021] Tianhe Yu, Aviral Kumar, Yevgen Chebo- tar, Karol Hausman, Sergey Levine, and Chelsea Finn. Conservative data sharing for multi-task offline reinforce- ment learning. Advances in Neural Information Process- ing Systems, 34:11501-11516, 2021.","title":"Conservative data sharing for multi-task offline reinforcement learning","authors":"Yu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yu et al., 2022] Tianhe Yu, Aviral Kumar, Yevgen Chebo- tar, Karol Hausman, Chelsea Finn, and Sergey Levine. How to leverage unlabeled data in offline reinforcement learning. In International Conference on Machine Learn- ing, pages 25611-25635. PMLR, 2022.","title":"How to leverage unlabeled data in offline reinforcement learning","authors":"Yu","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zenke et al., 2017] Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intel- ligence. In International Conference on Machine Learning (ICML), pages 3987-3995, 2017.","title":"Continual learning through synaptic intelligence","authors":"Friedemann Zenke, Ben Poole, Surya Ganguli","year":"2017","venue":"International Conference on Machine Learning (ICML)","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zintgraf et al., 2019] Luisa Zintgraf, Kyriacos Shiarli, Vi- taly Kurin, Katja Hofmann, and Shimon Whiteson. Fast context adaptation via meta-learning. In International Conference on Machine Learning, pages 7693-7702.","title":"Fast context adaptation via meta-learning","authors":"Luisa Zintgraf, Kyriacos Shiarli, Katja Vitaly Kurin, Shimon Hofmann, Whiteson","year":null,"venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"PMLR, 2019.","title":null,"authors":"Pmlr","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}