Safe Reinforcement Learning

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안녕하세요. 이동민입니다. :) 2018. 8. 9일에 한국항공우주연구원에서 발표한 "Safe Reinforcement Learning" 발표 자료입니다. 목차는 다음과 같습니다. 1. Reinforcement Learning 2. Safe Reinforcement Learning 3. Optimization Criterion 4. Exploration Process 강화학습 계속 공부하면서 실제로 많은 분들이 쓸 수 있게 하려면 더 안전하고 빨라야한다는 생각이 들었습니다. 그래서 이에 관련하여 논문과 각종 자료들로 공부하여 발표하였습니다. 많은 분들께 도움이 되었으면 좋겠습니다. 감사합니다!

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I updated the previous slides. Previous slides: https://www.slideshare.net/DongMinLee32/causal-confusion-in-imitation-learning-238882277 I reviewed the "Causal Confusion in Imitation Learning" paper. Paper link: https://papers.nips.cc/paper/9343-causal-confusion-in-imitation-learning.pdf - Abstract Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are non-causal: the training procedure is unaware of the causal structure of the interaction between the expert and the environment. We point out that ignoring causality is particularly damaging because of the distributional shift in imitation learning. In particular, it leads to a counter-intuitive “causal misidentification” phenomenon: access to more information can yield worse performance. We investigate how this problem arises, and propose a solution to combat it through targeted interventions—either environment interaction or expert queries—to determine the correct causal model. We show that causal misidentification occurs in several benchmark control domains as well as realistic driving settings, and validate our solution against DAgger and other baselines and ablations. - Outline 1. Introduction 2. Causality and Causal Inference 3. Causality in Imitation Learning 4. Experiments Setting 5. Resolving Causal Misidentification - Causal Graph-Parameterized Policy Learning - Targeted Intervention 6. Experiments Thank you!

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Title: Character Controllers using Motion VAEs Proceeding: ACM Transactions on Graphics (TOG) (Proc. SIGGRAPH 2020) Paper: https://dl.acm.org/doi/abs/10.1145/3386569.3392422 Video: https://www.youtube.com/watch?v=Zm3G9oqmQ4Y Given example motions, how can we generalize these to produce new purposeful motions? We take a two-step approach to this problem • Kinematic generative model based on an autoregressive conditional variational autoencoder or motion VAE (MVAE) • Learning controller to generate desired motions using Deep Reinforcement Learning (Deep RL)

Character Controllers using Motion VAEs

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Causal Confusion in Imitation Learning

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안녕하세요. RL korea에서 "GAIL하자!" 라는 프로젝트를 진행했던 프로젝트 매니저 이동민이라고 합니다. 이 자료는 저희가 4개월동안 진행했던 과정들을 간략하게 소개하는 자료입니다. 저희 프로젝트는 Imitation Learning의 방법 중 하나인 "Inverse RL"에 대한 논문들의 이론적 바탕을 이해하고 이를 환경에 구현해보는 프로젝트를 진행하였습니다. 관련 논문 리스트는 다음과 같습니다. [1] AY. Ng, et al., "Algorithms for Inverse Reinforcement Learning", ICML 2000. [2] P. Abbeel, et al., "Apprenticeship Learning via Inverse Reinforcement Learning", ICML 2004. [3] ND. Ratliff, et al., "Maximum Margin Planning", ICML 2006. [4] BD. Ziebart, et al., "Maximum Entropy Inverse Reinforcement Learning", AAAI 2008. [5] J. Ho, et al., "Generative Adversarial Imitation Learning", NIPS 2016. [6] XB. Peng, et al., "Variational Discriminator Bottleneck. Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow", ICLR 2019. 프로젝트 결과로는 논문을 정리한 블로그와 논문을 구현한 Github가 있습니다. 링크는 다음과 같습니다. - 블로그 : https://reinforcement-learning-kr.github.io/2019/01/22/0_lets-do-irl-guide/ - Github : https://github.com/reinforcement-learning-kr/lets-do-irl 우리 모두 함께 IRL해요! 감사합니다 :)

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안녕하세요. RL Korea에서 피지여행 프로젝트에 참여한 이동민입니다. 이 자료는 8월 25일 토요일에 제 1회 RLKorea 프로젝트 세미나에서 발표한 자료입니다. 아쉽게도 영상은 실행되지 않습니다. 세미나 링크는 아래와 같습니다. 제 1회 RLKorea 프로젝트 세미나 : https://www.facebook.com/groups/ReinforcementLearningKR/permalink/2024537701118793/?__tn__=H-R 피지여행을 간략하게 소개해드리자면, Policy Gradient와 관련된 논문들을 리뷰하여 블로그 정리하고 & 코드를 구현하여 실험하는 프로젝트입니다. 블로그와 깃허브는 다음과 같습니다. 정리 블로그 : https://reinforcement-learning-kr.github.io/…/0_pg-travel-…/ 구현 깃허브 : https://github.com/reinforcement-learning-kr/pg_travel 많은 분들이 보시고 도움이 되었으면 좋겠습니다! 감사합니다~~!!!

모두를 위한 PG여행 가이드

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Hello~! This material is reviewed by Dong-Min Lee. (http://www.facebook.com/dongminleeai) A reviewed material is Chapter 8. Planning and Learning with Tabular Methods in Reinforcement Learning: An Introduction written by Richard S. Sutton and Andrew G. Barto. Outline is 1) Introduction 2) Models and Planning 3) Dyna: Integrating Planning, Acting, and Learning 4) When the Model Is Wrong 5) Prioritized Sweeping 6) Expected vs. Sample Updates 7) Trajectory Sampling 8) Planning at Decision Time 9) Heuristic Search 10) Rollout Algorithms 11) Monte Carlo Tree Search 12) Summary I'm happy to be reviewed Sutton book~!!! I hope everyone who studies Reinforcement Learning sees this material and help! :) Thank you!

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안녕하세요. 강화학습을 공부하면서 처음 접하시는 분들을 위해 ppt로 '강화학습의 개요'에 대해서 정리했습니다. 동물이 학습하는 것과 똑같이 시행착오를 겪으면서 학습하는 강화학습은 기계학습 분야에서 상당히 매력적이라고 생각합니다. https://www.youtube.com/watch?v=PQtDTdDr8vs&feature=youtu.be 위의 링크는 스키너의 쥐 실험 영상입니다. 감사합니다.

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

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