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This system is based on reinforcement learning.
Consistent with a model based on reinforcement learning principles [Gureckis, T., & Love, B.C. Short term gains, long term pains: How cues about state aid learning in dynamic environments. Cognition (in press)], we find that learners differentially weight information predictive of the current task state.
To overcome this problem, other algorithms based on reinforcement learning techniques have been adopted in the literature [29].
We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL.
In the present work, we introduce a more sophisticated price update policy for the link manager agents, which is based on reinforcement learning.
In their paper "Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network," D. Houli et al. propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named, multi-RL.
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This work presents a modular neural-network model (based on reinforcement-learning actor critic methods) that tries to capture some of the most relevant known aspects of the role that basal ganglia play in learning and selecting motor behavior related to different goals.
Furthermore, we provide a simulation-based analysis based on a reinforcement learning model of bundling and unbundling strategies of service providers that participate in the auction.
Obviously, cognitive control can make handoff happen at an appropriate SNR level, which is based on the reinforcement learning.
Similarly, in [26], a self-tuning algorithm for FPC is proposed based on fuzzy-reinforcement learning techniques.
Learning in animals (and people) is often based on reinforcement.
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