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 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. Policy optimization (PO) is a key ingredient for reinforcement learning (RL). 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. Machine learnign really should be understood as an optimization problem. Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces, The papers “Provably Good Batch Reinforcement Learning Without Great Exploration” and “MOReL: Model-Based Offline Reinforcement Learning” tackle the same batch RL challenge. 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 … 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. Reinforcement learning is now the dominant paradigm for how an agent learns to interact with the world. 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. 155-167. 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 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] 2016. Ruosong Wang*, Simon S. Du*, Lin F. Yang*, Sham M. Kakade Conference on Neural Information Processing Systems (NeurIPS) 2020. 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. 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. 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. 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 RL is used to guide the MAV through complex environments where dead-end corridors may be encountered and backtracking … 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. ... [27], (distributionally) robust learning [63], and imitation learning [31, 15]. 1 Minimax Weight and Q-Function Learning for Off-Policy Evaluation. 10/21/2019 ∙ by Kaiqing Zhang, et al. v25 i2. 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." Swarm Intelligence is a set of learning and biologically-inspired approaches to solve hard optimization problems using distributed cooperative agents. RISK-SENSITIVE REINFORCEMENT LEARNING 269 The main contribution of the present paper are the following. 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. If you find this repository helpful in your publications, please consider citing our paper. Owing to the computationally intensive nature of such problems, it is of interest to obtain provable guarantees for first-order optimization methods. Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? 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. 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