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In this paper, we develop a novel incremental learning scheme for reinforcement learning (RL) in dynamic environments, where the reward functions may change over time instead of being static. The proposed incremental learning scheme aims at automatically adjusting the optimal policy in order to adapt to the ever-changing environment. We evaluate the proposed scheme on a classical maze navigation problem...
Robust control design for operation of quantum systems has been considered as a demanding and challenging task in the development of quantum technologies. In this paper, we apply the sampling-based learning control (SLC) approach to design a control law for manipulating two-level quantum systems with uncertainties. The gradient-based learning and optimization algorithm is adopted to find the optimal...
In this paper, a quantum-inspired reinforcement learning (QiRL) algorithm is proposed for self-configuring network services in next generation networks. A new learning and adaptation scheme based on QiRL facilitates the optimal operation for multiple classes of managed elements on a network Operations Support Systems (OSSs). QiRL algorithm adopts a probabilistic action selection policy and a new reinforcement...
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