Results 41 to 50 of about 93,202 (286)
Least-squares methods for policy iteration [PDF]
, 2011 Approximate reinforcement learning deals with the essential problem of applying reinforcement learning in large and continuous state-action spaces, by using function approximators to represent the solution.
Babuska, Robert +5 more
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Tuning approximate dynamic programming policies for ambulance redeployment via direct search
Stochastic Systems, 2014 In this paper we consider approximate dynamic programming methods for ambulance redeployment. We first demonstrate through simple examples how typical value function fitting techniques, such as approximate policy iteration and linear programming, may not
Matthew S. Maxwell +2 more
doaj +1 more source
Applied Sciences, 2021 In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties.
Dengguo Xu, Qinglin Wang, Yuan Li
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Observer-Based Adaptive Control of Uncertain Nonlinear Systems Via Neural Networks
IEEE Access, 2018 In this paper, a novel observer-based control strategy is proposed for a class of uncertain continuous-time nonlinear systems based on the Hamilton-Jacobi-Bellman (HJB) equation.
Chaoxu Mu, Yong Zhang, Ke Wang
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Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics [PDF]
Journal of Artificial Intelligence and Data Mining, 2015 In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information.
F. Tatari, M. B. Naghibi-Sistani
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Application of machine learning to assess the value of information in polymer flooding
Petroleum Research, 2021 In this work, we provide a more consistent alternative for performing value of information (VOI) analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir decision-making.
Amine Tadjer +3 more
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Newton’s method for reinforcement learning and model predictive control
Results in Control and Optimization, 2022 The purpose of this paper is to propose and develop a new conceptual framework for approximate Dynamic Programming (DP) and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely independently of each other ...
Dimitri Bertsekas
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Near-Optimal Tracking Control of a Nonholonomic Mobile Robot with Uncertainties
International Journal of Advanced Robotic Systems, 2012 A combined kinematic/torque control law is developed by using a backstepping design approach for a nonholonomic mobile robot with two driving wheels mounted on the same axis to track a reference trajectory.
Kai Wang
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Value-iteration based fitted policy iteration: learning with a single trajectory [PDF]
, 2007 ADPRL 2007. Honolulu, Hawaii, Apr 1-5, 2007. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems when the training data is composed of the trajectory of some fixed behaviour
Antos, András +2 more
core +3 more sources
Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
IEEE Open Journal of Vehicular Technology, 2020 The fifth generation and beyond wireless communication will support vastly heterogeneous services and user demands such as massive connection, low latency and high transmission rate.
Fei Song +5 more
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