{"type":"ScholarlyArticle","title":"Generalized Representation Learning Methods for Deep Reinforcement Learning","authors":[{"name":"Hanhua Zhu","slug":"hanhua-zhu","given":"Hanhua","family":"Zhu","affiliation":"The University of Tokyo , Japan"}],"abstract":"Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.","doi":"10.24963/ijcai.2020/748","url":"https://ijcai.org/proceedings/2020/748","pdf":"https://ijcai.org/proceedings/2020/0748.pdf","markdown":"https://ijcai.org/proceedings/2020/748.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/748","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":748,"pages":{"first":5216,"last":5217},"keywords":["Heuristic Search and Game Playing: Game Playing and Machine Learning","Machine Learning: Deep Reinforcement Learning","Machine Learning: Probabilistic Machine Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24720/file_24720.m3u8","length_sec":252,"provider":"hls"}],"references":[{"position":1,"raw":"[Doerr et al., 2018] Andreas Doerr, Christian Daniel, and Martin Schiegg et al. Probabilistic recurrent state-space models. arXiv preprint arXiv:1801.10395, 2018.","title":"Probabilistic recurrent state-space models","authors":"Andreas Doerr, Christian Daniel, Martin Schiegg","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Espeholt et al., 2018] Lasse Espeholt, Hubert Soyer, and Remi Munos et al. Impala: Scalable distributed deep- rl with importance weighted actor-learner architectures. arXiv preprint arXiv:1802.01561, 2018.","title":"Impala: Scalable distributed deeprl with importance weighted actor-learner architectures","authors":"Espeholt","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Lee et al., 2019] Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine. Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model. arXiv preprint arXiv:1907.00953, 2019.","title":"Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model","authors":"Alex X Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, and David Silver et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.","title":"Volodymyr Mnih, Koray Kavukcuoglu, and David Silver et al. Human-level control through deep reinforcement learning","authors":"Mnih","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Nair et al., 2018] Ashvin V Nair, Vitchyr Pong, and Mur- taza Dalal et al. Visual reinforcement learning with imag- ined goals. In Advances in Neural Information Processing Systems, pages 9191-9200, 2018.","title":"Visual reinforcement learning with imagined goals","authors":"Vitchyr Ashvin V Nair, Murtaza Pong, Dalal","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Wang et al., 2017] Fei Wang, Mengqing Jiang, and Chen Qian et al. Residual attention network for image clas- sification. In 2017 IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 6450-6458. IEEE, 2017.","title":"Residual attention network for image classification","authors":"Fei Wang, Mengqing Jiang, Chen Qian","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Zhang et al., 2018] Marvin Zhang, Sharad Vikram, and Laura Smith et al. Solar: deep structured representations for model-based reinforcement learning. arXiv preprint arXiv:1808.09105, 2018.","title":"Solar: deep structured representations for model-based reinforcement learning","authors":"Marvin Zhang, Sharad Vikram, Laura Smith","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Zhu and Kaneko, 2019] Hanhua Zhu and Tomoyuki Kaneko. Deep residual attention reinforcement learning. In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pages 1-6. IEEE, 2019.","title":"Deep residual attention reinforcement learning","authors":"Kaneko ; ] Hanhua Zhu, Tomoyuki Zhu, Kaneko","year":"2019","venue":"2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}