Results 31 to 40 of about 6,930,474 (290)
Deep Reinforcement Learning Under Signal Temporal Logic Constraints Using Lagrangian Relaxation
IEEE Access, 2022 Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems. On the other hand, in general, constraints may be imposed on optimal control problems.
Junya Ikemoto, Toshimitsu Ushio
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Frontiers in Energy Research, 2022 As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted ...
Han Cui +6 more
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Reinforcement learning for imitating constrained reaching movements [PDF]
Advanced Robotics, 2007 The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots that can be accomplished by everyone. When a demonstrator teaches a task to a robot, he/she shows some ways of fulfilling the task, but not all the possibilities. The robot must then be able to reproduce the task even when unexpected
Guenter, F. +3 more
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Constrained Combinatorial Optimization with Reinforcement Learning
CoRR, 2020 This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL). To this end, we extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation.
Ruben Solozabal +2 more
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Constrained Upper Confidence Reinforcement Learning
CoRR, 2020 Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints. This paper extends upper confidence reinforcement learning for settings in which the reward function and the constraints, described by cost functions, are unknown a priori but the ...
Liyuan Zheng, Lillian J. Ratliff
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Machines, 2023 Cascaded control structures are prevalent in industrial systems with many disturbances to obtain stable control but are cumbersome and challenging to tune.
Tom Staessens +2 more
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Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
IEEE Transactions on Machine Learning in Communications and Networking, 2023 The problem of multiclass scheduling in a dynamic wireless setting is considered here, where the available limited bandwidth resources are allocated to handle random service demand arrivals, belonging to different classes in terms of payload data request,
Apostolos Avranas +2 more
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Constrained Policy Improvement for Efficient Reinforcement Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 We propose a policy improvement algorithm for Reinforcement Learning (RL) termed Rerouted Behavior Improvement (RBI). RBI is designed to take into account the evaluation errors of the Q-function. Such errors are common in RL when learning the Q-value from finite experience data.
Elad Sarafian, Aviv Tamar, Sarit Kraus
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Adaptable bandwidth planning using reinforcement learning [PDF]
Journal of Systemics, Cybernetics and Informatics, 2006 In order to improve the bandwidth allocation considering feedback of operational environment, adaptable bandwidth planning based on reinforcement learning is proposed.
Dirk Hetzer
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Probabilistic Constrained Reinforcement Learning with Formal Interpretability
, 2023 Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and the corresponding optimal policy. Consequently, representing sequential decision-making problems as probabilistic
Wang, Y, Qian, Q, Boyle, D
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