… This poses new challenges to the existing optimization-… learning methods, particularly deep
reinforcement learning (DRL… to investigate practical phase shift design and optimization of a …

… We further show that quantum-state manipulation viewed as an optimization problem
exhibits a spin-glass-like phase transition in the space of protocols as a function of the protocol …

… optimization scheme with TPS modulation using RL is proposed. The main objective is to
obtain the optimal phase-… 1) An optimized modulation strategy can be obtained by using the Q-…

… optimization of the passive phase … reinforcement learning (DRL) on resolving complicated
control problems, we develop a DRL based framework to solve this non-convex optimization …

… paper, a two-phase neural combinatorial optimization method with reinforcement learning
is … First, a neural combinatorial optimization with the reinforcement learning method is …

… In Section V, we explain how we apply the path integral reinforcement learning method to
the CPG-based biped controller. Finally, in Section VI, we show the results of the learning …

… (RL) [36] is considered to be one of the approaches that provides adaptive optimization …
decentralized optimization problems. We aim to use Collaborative Reinforcement Learning (CRL…

… neural network) can be applied to optimize field development planning for a range of …
reinforcement learning technique considered in this work for the field development optimization …

… called Phase-Aware Deep Learning and Constrained Reinforcement Learning for optimization
… Constrained Reinforcement Learning concept is the main contribution of this work and it …

… solve industrial engineering optimization problems. Given the characteristics of TLBO,
reinforcement learning (RL) in machine learning is introduced to the learner phase, and enables …