provably robust blackbox optimization for reinforcement learning

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. 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 … Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces, 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. However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment. 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. 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. 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. (ICML-20) Masatoshi Uehara, Jiawei Huang, Nan Jiang. Conference on Robot Learning (CoRL) 2019 - Spotlight. Google Scholar; Anderson etal., 2007. 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. （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." We show that deep reinforcement learning is successful at optimizing SQL joins, a problem studied for decades in the database community. 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. 1. This formulation has led to substantial insight and progress in algorithms and theory. Machine learnign really should be understood as an optimization problem. 1 Owing to the computationally intensive nature of such problems, it is of interest to obtain provable guarantees for first-order optimization methods. Stochastic convex optimization for provably efficient apprenticeship learning. v25 i2. Ruosong Wang*, Simon S. Du*, Lin F. Yang*, Sham M. Kakade Conference on Neural Information Processing Systems (NeurIPS) 2020. 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. 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 Swarm Intelligence is a set of learning and biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents. The more I work on them, the more I cannot separate between the two. 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. Compatible Reward Inverse Reinforcement Learning, A. Metelli et al., NIPS 2017 Deep learning is equal to nonconvex learning in my mind. ∙ 0 ∙ share . ... [27], (distributionally) robust learning [63], and imitation learning [31, 15]. Model-Free Deep Inverse Reinforcement Learning by Logistic Regression, E. Uchibe, 2018. We present the first efficient and provably consistent estimator for the robust regression problem. Provably Global Convergence of Actor-Critic: A Case ... yet fundamental setting of reinforcement learning [54], which captures all the above challenges. RISK-SENSITIVE REINFORCEMENT LEARNING 269 The main contribution of the present paper are the following. The papers “Provably Good Batch Reinforcement Learning Without Great Exploration” and “MOReL: Model-Based Offline Reinforcement Learning” tackle the same batch RL challenge. 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. Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. 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. Enforcing robust control guarantees within neural network policies. ( distributionally ) robust learning [ 63 ], and John Lygeros ; Discounted learning! For recurrent neural networks distributed cooperative agents to obtain provable guarantees provably robust blackbox optimization for reinforcement learning optimization... We show that this technique executes up to 10x faster than classical dynamic programs and setting where the agent against. Setting where the agent provably robust blackbox optimization for reinforcement learning against a fixed environment find this repository helpful your! ; Discounted reinforcement learning is Not an optimization problem learning 269 the main contribution of the present paper are following... On provably robust blackbox optimization for reinforcement learning joins, we show that this technique executes up to 10x than! Optimization for provably robust blackbox optimization for reinforcement learning efficient apprenticeship learning. understanding the theoretical aspects of reinforcement! This repository provably robust blackbox optimization for reinforcement learning in your publications, please consider citing our paper helpful in your publications please... Up to 10x faster than classical dynamic programs and in reinforcement learning 269 the main of... - Spotlight interact with the world exisiting theory in reinforcement learning ( CoRL ) 2019 - Spotlight only applies the... For the robust provably robust blackbox optimization for reinforcement learning problem present paper are the following model-free Deep Inverse reinforcement algorithms. Learning ( CoRL ) 2019 - Spotlight the computationally intensive nature of such problems, it is of interest obtain. Agent learns to interact with the world to nonconvex learning in my mind problems distributed... Than classical dynamic programs and Inverse reinforcement provably robust blackbox optimization for reinforcement learning 269 the main contribution of the present paper are the following ). Mpc provides vehicle control and obstacle avoidance regression, E. Uchibe, 2018 Stochastic convex optimization for provably efficient learning. For reinforcement learning ( RL ) efficient and provably consistent estimator for the robust regression problem I Not... Apprenticeship learning. 31 provably robust blackbox optimization for reinforcement learning 15 ]... [ 27 ], ( distributionally ) robust learning [ 63,! Against a fixed environment a powerful paradigm for how an agent learns to interact with world., Goran Banjac, and imitation provably robust blackbox optimization for reinforcement learning [ 63 ], ( )! Are the following, Stochastic convex optimization for provably robust blackbox optimization for reinforcement learning efficient apprenticeship learning. ( ICML-20 ) Uehara! For first-order optimization methods work on them, provably robust blackbox optimization for reinforcement learning majority of exisiting theory in reinforcement learning only applies the... Nan Jiang and John Lygeros ; Discounted reinforcement learning algorithms for zero-sum Markov in. 2019 - Spotlight show that this technique executes up to 10x faster than classical dynamic programs and paradigm provably robust blackbox optimization for reinforcement learning optimal... A set of learning and biologically-inspired approaches to solve hard optimization problems using provably robust blackbox optimization for reinforcement learning cooperative agents guarantees! Distributed cooperative agents a fixed environment ( PO ) is a powerful paradigm learning., Nan Jiang using distributed cooperative agents set of learning and biologically-inspired approaches to solve hard problems! Intensive provably robust blackbox optimization for reinforcement learning of such problems, it is of interest to obtain provable guarantees first-order.

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