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. 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." 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. v25 i2. 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. RISK-SENSITIVE REINFORCEMENT LEARNING 269 The main contribution of the present paper are the following. Model-Free Deep Inverse Reinforcement Learning by Logistic Regression, E. Uchibe, 2018. 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 Conference on Robot Learning (CoRL) 2019 - Spotlight. 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. 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 Stochastic convex optimization for provably efficient apprenticeship learning. 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 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. 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. Robust adaptive MPC for constrained uncertain nonlinear systems. 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." Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. 155-167. 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. 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. （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." 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. Stochastic Flows and Geometric Optimization on the Orthogonal Group Owing to the computationally intensive nature of such problems, it is of interest to obtain provable guarantees for first-order optimization methods. 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 However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment. 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. ... [27], (distributionally) robust learning [63], and imitation learning [31, 15]. The more I work on them, the more I cannot separate between the two. Angeliki Kamoutsi, Angeliki Kamoutsi, Goran Banjac, and John Lygeros; Discounted Reinforcement Learning is Not an Optimization Problem. Compatible Reward Inverse Reinforcement Learning, A. Metelli et al., NIPS 2017 Machine learnign really should be understood as an optimization problem. From Importance Sampling to Doubly Robust … Enforcing robust control guarantees within neural network policies. 来自 … 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. Swarm Intelligence is a set of learning and biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents. 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. Deep learning is equal to nonconvex learning in my mind. 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 Such instances of minimax optimization remain challenging as they lack convexity-concavity in general Provably Robust Blackbox Optimization for Reinforcement Learning, with Krzysztof Choromanski, Jack Parker Holder, Jasmine Hsu, Atil Iscen, Deepali Jain and Vikas Sidhwani. International Journal of Adaptive Control and Signal Processing. Provably Efficient Exploration for RL with Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? Minimax Weight and Q-Function Learning for Off-Policy Evaluation. 993-1002. IEEE Transactions on Neural Networks. We show that deep reinforcement learning is successful at optimizing SQL joins, a problem studied for decades in the database community. 10/21/2019 ∙ by Kaiqing Zhang, et al. 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. 1 Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, J. Fu et al., 2018. Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces, Policy Optimization for H_2 Linear Control with H_∞ Robustness Guarantee: Implicit Regularization and Global Convergence. 2016. Robust reinforcement learning control using integral quadratic constraints for recurrent neural networks. Provably Global Convergence of Actor-Critic: A Case ... yet fundamental setting of reinforcement learning [54], which captures all the above challenges. If you find this repository helpful in your publications, please consider citing our paper. Reinforcement learning is now the dominant paradigm for how an agent learns to interact with the world. Policy optimization (PO) is a key ingredient for reinforcement learning (RL). 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. 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 1. The papers “Provably Good Batch Reinforcement Learning Without Great Exploration” and “MOReL: Model-Based Offline Reinforcement Learning” tackle the same batch RL challenge. 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. 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. An efficient implementation of MPC provides vehicle control and obstacle avoidance. 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. Reinforcement learning is the problem of building systems that can learn behaviors in an environment, based only on an external reward. v18 i4. Further, on large joins, we show that this technique executes up to 10x faster than classical dynamic programs and … We present the first efficient and provably consistent estimator for the robust regression problem. RL is used to guide the MAV through complex environments where dead-end corridors may be encountered and backtracking … This formulation has led to substantial insight and progress in algorithms and theory. 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 Writing robust machine learning programs is a combination of many aspects ranging from accurate training dataset to efficient optimization techniques. 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] ∙ 0 ∙ share . Ruosong Wang*, Simon S. Du*, Lin F. Yang*, Sham M. Kakade Conference on Neural Information Processing Systems (NeurIPS) 2020. (UAI-20) Tengyang Xie, Nan Jiang. Google Scholar; Anderson etal., 2007. Abhishek Naik, Roshan Shariff, Niko Yasui, Richard Sutton; This page was generated by … Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. 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 … Reinforcement Learning paradigm. (ICML-20) Masatoshi Uehara, Jiawei Huang, Nan Jiang. Formulation has led to substantial insight and progress in algorithms and theory ( RL ) serves..., the majority of exisiting theory in reinforcement learning only applies to the where! 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