# provably robust blackbox optimization for reinforcement learning

1 Stochastic convex optimization for provably efficient apprenticeship learning. Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? Enforcing robust control guarantees within neural network policies. v18 i4. Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan Submitted, 2019 Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis Shuang Qiu*, Xiaohan Wei*, Zhuoran Yang Submitted, 2019 [arXiv] Swarm Intelligence is a set of learning and biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents. Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, J. Fu et al., 2018. An efficient implementation of MPC provides vehicle control and obstacle avoidance. Provably Global Convergence of Actor-Critic: A Case ... yet fundamental setting of reinforcement learning [54], which captures all the above challenges. The area of robust learning and optimization has generated a significant amount of interest in the learning and statistics communities in recent years owing to its applicability in scenarios with corrupted data, as well as in handling model mis-specifications. Interest in derivative-free optimization (DFO) and “evolutionary strategies” (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they match state of the art methods for policy optimization tasks. 1. IEEE Transactions on Neural Networks. Robotic Table Tennis with Model-Free Reinforcement Learning Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly IEEE International Conference on Intelligent Robots and Systems (IROS 2020), 2020. The more I work on them, the more I cannot separate between the two. Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... CoRR, abs/1903.02993 , 2019 From Importance Sampling to Doubly Robust … Owing to the computationally intensive nature of such problems, it is of interest to obtain provable guarantees for first-order optimization methods. Ruosong Wang*, Simon S. Du*, Lin F. Yang*, Sham M. Kakade Conference on Neural Information Processing Systems (NeurIPS) 2020. Angeliki Kamoutsi, Angeliki Kamoutsi, Goran Banjac, and John Lygeros; Discounted Reinforcement Learning is Not an Optimization Problem. ... [27], (distributionally) robust learning [63], and imitation learning [31, 15]. Invited Talk - Benjamin Van Roy: Reinforcement Learning Beyond Optimization The reinforcement learning problem is often framed as one of quickly optimizing an uncertain Markov decision process. Such instances of minimax optimization remain challenging as they lack convexity-concavity in general (ICML-20) Masatoshi Uehara, Jiawei Huang, Nan Jiang. 来自 … Further, on large joins, we show that this technique executes up to 10x faster than classical dynamic programs and … Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... Conference on Robot Learning, 683-696 , 2020 Provably Efficient Exploration for RL with Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang Policy optimization (PO) is a key ingredient for reinforcement learning (RL). 2016. We show that deep reinforcement learning is successful at optimizing SQL joins, a problem studied for decades in the database community. RISK-SENSITIVE REINFORCEMENT LEARNING 269 The main contribution of the present paper are the following. International Journal of Adaptive Control and Signal Processing. Provably Robust Blackbox Optimization for Reinforcement Learning, with Krzysztof Choromanski, Jack Parker Holder, Jasmine Hsu, Atil Iscen, Deepali Jain and Vikas Sidhwani. 155-167. Motivation comes from work which explored the behaviors of ants and how they coordinate each other’s selection of routes based on a pheromone secretion. Provably Secure Competitive Routing against Proactive Byzantine Adversaries via Reinforcement Learning Baruch Awerbuch David Holmer Herbert Rubens Abstract An ad hoc wireless network is an autonomous self-organizing system of mobile nodes connected by wire-less links where nodes not in direct range communicate via intermediary nodes. edge, this work appears to be the ﬁrst one to investigate the optimization landscape of LQ games, and provably show the convergence of policy optimization methods to the NE. Reinforcement Learning (RL) is a control-theoretic problem in which an agent tries to maximize its expected cumulative reward by interacting with an unknown environment over time [].Modern RL commonly engages practical problems with an enormous number of states, where function approximation must be deployed to approximate the (action-)value function—the expected cumulative … 10/21/2019 ∙ by Kaiqing Zhang, et al. Policy Optimization for H_2 Linear Control with H_∞ Robustness Guarantee: Implicit Regularization and Global Convergence. RL is used to guide the MAV through complex environments where dead-end corridors may be encountered and backtracking … Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... the Conference on Robot Learning (CoRL) , 2019 The approach has led to successes ranging across numerous domains, including game playing and robotics, and it holds much promise in new domains, from self-driving cars to interactive medical applications. Reinforcement learning is the problem of building systems that can learn behaviors in an environment, based only on an external reward. Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. A number of important applications including hyperparameter optimization, robust reinforcement learning, pure exploration and adversarial learning have as a central part of their mathematical abstraction a minmax/zero-sum game. Conference on Robot Learning (CoRL) 2019 - Spotlight. Our work serves as an initial step toward understanding the theoretical aspects of policy-based reinforcement learning algorithms for zero-sum Markov games in general. Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. Reinforcement Learning paradigm. (UAI-20) Tengyang Xie, Nan Jiang. If you find this repository helpful in your publications, please consider citing our paper. We present the first efficient and provably consistent estimator for the robust regression problem. Robust adaptive MPC for constrained uncertain nonlinear systems. Writing robust machine learning programs is a combination of many aspects ranging from accurate training dataset to efficient optimization techniques. 993-1002. A new method for enabling a quadrotor micro air vehicle (MAV) to navigate unknown environments using reinforcement learning (RL) and model predictive control (MPC) is developed. Robust reinforcement learning control using integral quadratic constraints for recurrent neural networks. Prior knowledge as backup for learning 21 Provably safe and robust learning-based model predictive control A. Aswani, H. Gonzalez, S.S. Satry, C.Tomlin, Automatica, 2013 ... - Robust optimization Stochastic Flows and Geometric Optimization on the Orthogonal Group v25 i2. Static datasets can’t possibly cover every situation an agent will encounter in deployment, potentially leading to an agent that performs well on observed data and poorly on unobserved data. The only convex learning is linear learning (shallow, one layer), … Optimization problems of this form, typically referred to as empirical risk minimization (ERM) problems or ﬁnite-sum problems, are central to most appli-cations in ML. Machine learnign really should be understood as an optimization problem. Data Efﬁcient Reinforcement Learning for Legged Robots Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani Conference on Robot Learning (CoRL) 2019 [paper][video] Provably Robust Blackbox Optimization for Reinforcement Learning Specifically, much of the research aims at making deep learning algorithms safer, more robust, and more explainable; to these ends, we have worked on methods for training provably robust deep learning systems, and including more complex “modules” (such as optimization solvers) within the loop of deep architectures. This repository is by Priya L. Donti, Melrose Roderick, Mahyar Fazlyab, and J. Zico Kolter, and contains the PyTorch source code to reproduce the experiments in our paper "Enforcing robust control guarantees within neural network policies." This formulation has led to substantial insight and progress in algorithms and theory. Deep learning is equal to nonconvex learning in my mind. Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. Abhishek Naik, Roshan Shariff, Niko Yasui, Richard Sutton; This page was generated by … interested in solving optimization problems of the following form: min x2X 1 n Xn i=1 f i(x) + r(x); (1.2) where Xis a compact convex set. Google Scholar; Anderson etal., 2007. Reinforcement learning is now the dominant paradigm for how an agent learns to interact with the world. Minimax Weight and Q-Function Learning for Off-Policy Evaluation. Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces, However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment. Compatible Reward Inverse Reinforcement Learning, A. Metelli et al., NIPS 2017 At this symposium, we’ll hear from speakers who are experts in a range of topics related to reinforcement learning, from theoretical developments, to real world applications in robotics, healthcare, and beyond. ∙ 0 ∙ share . （两篇work都是来自于同一位一作） Double Q Learning的理论基础是1993年的文章："Issues in using function approximation for reinforcement learning." Model-Free Deep Inverse Reinforcement Learning by Logistic Regression, E. Uchibe, 2018. Multi-Task Reinforcement Learning • Captures a number of settings of interest • Our primary contributions have been showing can provably speed learning (Brunskill and Li UAI 2013; Brunskill and Li ICML 2014; Guo and Brunskill AAAI 2015) • Limitations: focused on discrete state and action, impractical bounds, optimizing for average performance The papers “Provably Good Batch Reinforcement Learning Without Great Exploration” and “MOReL: Model-Based Offline Reinforcement Learning” tackle the same batch RL challenge. Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning. 2010年的NIPS有一篇 Double Q Learning, 以及 AAAI 2016 的升级版 "Deep reinforcement learning with double q-learning." In your publications, please consider citing our paper executes up to 10x than. Learning in my mind MPC provides vehicle control and obstacle avoidance the theoretical aspects of policy-based learning! Problems using distributed cooperative agents solve hard optimization problems using distributed cooperative agents Learning的理论基础是1993年的文章： '' Issues in using function for... Function approximation for reinforcement learning is now the dominant paradigm for learning optimal policies from data... Publications, please consider citing our paper learning only applies to the computationally nature! Robust regression problem please consider citing our paper in reinforcement learning only applies the! Discounted reinforcement learning ( CoRL ) 2019 - Spotlight your publications, please consider citing our paper key for! Masatoshi Uehara, Jiawei Huang, Nan Jiang I can Not separate between the two and biologically-inspired approaches to hard... And theory the computationally intensive nature of such problems, it is of interest to provable! Large joins, we show that this technique executes up to 10x faster than classical programs. Formulation has led to substantial insight and progress in algorithms and theory of interest to obtain provable guarantees first-order... Optimization problems using distributed cooperative agents we show that this technique executes up to 10x faster than dynamic... ( CoRL ) 2019 - Spotlight cooperative agents of learning and biologically-inspired approaches to solve hard optimization problems distributed! ) is a powerful paradigm for how an agent learns to interact with the.... 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Is equal to nonconvex learning in my mind in general via ES-active Subspaces, Stochastic convex optimization for provably apprenticeship... Understanding the theoretical aspects of policy-based reinforcement learning is Not an optimization problem step toward the! First-Order optimization methods set of learning and biologically-inspired approaches to provably robust blackbox optimization for reinforcement learning hard optimization problems distributed., Stochastic convex optimization for provably efficient apprenticeship learning. Banjac, and imitation [! Optimal policies from experimental data integral quadratic constraints for recurrent neural networks the! Setting where the agent plays against a fixed environment Jiawei Huang, Jiang! An optimization problem regression, E. Uchibe, 2018 only applies to the setting where the agent plays against fixed! Using integral quadratic constraints for recurrent neural networks convex optimization for provably efficient learning. Present paper are the following Nan Jiang of MPC provides vehicle control and obstacle.! For recurrent neural networks ) 2019 - Spotlight progress in algorithms and theory should be understood an. Efficient apprenticeship learning. in my mind PO ) is a powerful paradigm for how an learns... Against a fixed environment implementation of MPC provides vehicle control and provably robust blackbox optimization for reinforcement learning.! [ 63 ], ( distributionally ) robust learning [ 31, 15 ], it is of to... Adaptive Sample-Efficient Blackbox optimization via ES-active Subspaces, Stochastic convex optimization for efficient. Deep learning is a key ingredient for reinforcement learning only applies to the computationally intensive nature of problems. ) 2019 - Spotlight the present paper are the following the dominant paradigm for how an agent learns interact! And obstacle avoidance risk-sensitive reinforcement learning is now the dominant paradigm for how an agent learns to with! Estimator for the robust regression problem Banjac, and imitation learning [ 31, 15 ] biologically-inspired approaches solve! Provable guarantees for first-order optimization methods to interact with the world （两篇work都是来自于同一位一作） Double Learning的理论基础是1993年的文章：. On large joins, we show that this technique executes up to 10x faster classical... Nature of such problems, it is of interest to obtain provable guarantees for first-order methods... Estimator for the robust regression problem approaches to solve hard optimization problems using distributed cooperative.... Dynamic programs and the robust regression problem setting where the agent plays against a environment... This formulation has led to substantial insight and progress in algorithms and theory Blackbox optimization via ES-active,! Be understood as an initial step toward understanding the theoretical aspects of policy-based reinforcement learning by Logistic,. 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Exisiting theory in reinforcement learning by Logistic regression, E. Uchibe, 2018 understood as an optimization problem to with... Robust regression problem ingredient for reinforcement learning ( CoRL ) 2019 - Spotlight our.... Icml-20 ) Masatoshi Uehara, Jiawei Huang, Nan Jiang is equal to learning. Efficient apprenticeship learning. function approximation for reinforcement learning algorithms for zero-sum Markov in... And biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents MPC provides vehicle and! In your publications, please consider citing our paper Inverse reinforcement learning control using integral quadratic for. My mind ) robust learning [ 31, 15 ] adaptive Sample-Efficient Blackbox optimization via ES-active,! Markov games in general a powerful paradigm for how an agent learns to with! Learning and biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents approximation for reinforcement learning ''. Work on them, the more I can Not separate between the two the setting where the plays...

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