第32回強化学習アーキテクチャ勉強会状態表現学習と世界モデルの最近の研究，および深層生成モデルライブラリPixyzの紹介 #rlarch

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ঢ়ଶදݱֶशͱੈքϞσϧͷ࠷ۙͷݚڀ  ͓Αͼਂ૚ੜ੒ϞσϧϥΠϒϥϦPixyzͷ঺հ 1 ౦ژେֶ ޻ֶܥݚڀՊ म࢜՝ఔ1೥ দౢ ୡ໵ (Tatsuya Matsushima) @__tmats__

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References [Agrawal+ 2016] Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine (2016). Learning to Poke by Poking: Experiential Learning of Intuitive Physics. https://arxiv.org/abs/1606.07419 [Bengio+ 2013] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. https://ieeexplore.ieee.org/document/6472238 [Böhmer+ 2015] Böhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M., and Obermayer, K. (2015). Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations. KI - Künstliche Intelligenz, pages 1–10. http://www.ni.tu-berlin.de/fileadmin/fg215/articles/ boehmer15b.pdf [Curran+ 2015] William Curran, Tim Brys, Matthew Taylor, William Smart (2015). Using PCA to Efficiently Represent State Spaces. https:// arxiv.org/abs/1505.00322 [Finn+ 2015] Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel (2015). Deep Spatial Autoencoders for Visuomotor Learning. https://arxiv.org/abs/1509.06113 [Ha+ 2018] David Ha, Jürgen Schmidhuber (2018). World Models. https://arxiv.org/abs/1803.10122 [Higgins+ 2016] Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner (2016). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. https:// openreview.net/forum?id=Sy2fzU9gl [Jonschkowski+ 2015] Jonschkowski, R. and Brock, O. (2015). Learning state representations with robotic priors. Auton. Robots, 39(3): 407–428. http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/Jonschkowski-15-AURO.pdf [Jonschkowski+ 2017] Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin Riedmiller (2017). PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations. https://arxiv.org/abs/1705.09805 [Karl+ 2016] Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. https://arxiv.org/abs/1605.06432 42

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References [Kurutach+ 2018] Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel (2018). Learning Plannable Representations with Causal InfoGAN. https://arxiv.org/abs/1807.09341 [Lake+ 2016} Building Machines That Learn and Think Like People (2016). Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman. https://arxiv.org/abs/1604.00289 [Oh+ 2017] Junhyuk Oh, Satinder Singh, Honglak Lee (2017). Value Prediction Network. https://arxiv.org/abs/1707.03497 [Pathak+ 2017] Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell (2017). Curiosity-driven Exploration by Self- supervised Prediction. https://arxiv.org/abs/1705.05363 [Raffin+ 2018] Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat (2018). S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning. https://arxiv.org/abs/1809.09369 [Lesort+ 2017] Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat (2017). Unsupervised state representation learning with robotic priors: a robustness benchmark. https://arxiv.org/abs/1709.05185 [Mattner+ 2012] Mattner, J., Lange, S., and Riedmiller, M. A. (2012). Learn to swing up and balance a real pole based on raw visual input data. In Neural Information Processing - 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V, pages 126–133. https://ieeexplore.ieee.org/document/7759578 [van Hoof+ 2016] van Hoof, H., Chen, N., Karl, M., van der Smagt, P., and Peters, J. (2016). Stable reinforcement learning with autoencoders for tactile and visual data. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3928–3934. https://ieeexplore.ieee.org/document/7759578/ [Watter+ 2015] Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller (2015). Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. https://arxiv.org/abs/1506.07365 43

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References [Eslami+ 2018] Eslami, S. M. Ali, Danilo Jimenez Rezende, Frédéric Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil C. Rabinowitz, Helen King, Chloe Hillier, Matthew M Botvinick, Daan Wierstra, Koray Kavukcuoglu and Demis Hassabis. “Neural scene representation and rendering.” Science 360 (2018): 1204-1210. http:// science.sciencemag.org/content/360/6394/1204 [Gregor+ 2015] Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra. DRAW: A Recurrent Neural Network For Image Generation. https://arxiv.org/abs/1502.04623 [Gregor+ 2016] Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra. Towards Conceptual Compression. https://arxiv.org/abs/1604.08772 [Gregor+ 2019] Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber. Temporal Difference Variational Auto-Encoder. https://openreview.net/forum?id=S1x4ghC9tQ [Kingma+ 2014] Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling. Semi-Supervised Learning with Deep Generative Models. https://arxiv.org/abs/1406.5298 [Sohn+ 2015] Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional generative models. In Advances in Neural Information Processing Systems (NIPS), pp. 3483–3491, 2015. https://papers.nips.cc/ paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models [Tschannen+ 2018] Michael Tschannen, Olivier Bachem, Mario Lucic (2018). Recent Advances in Autoencoder-Based Representation Learning. https://arxiv.org/abs/1812.05069 [Wagstaff+ 2019] Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne. On the Limitations of Representing Functions on Sets. https://arxiv.org/abs/1901.09006 102

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