reinforcement learning
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial for ensuring the feasibility and safety of actions in real-world systems. We evaluate existing algorithms and their novel variants across multiple robotics control environments, encompassing multiple action constraint types.
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Abstract—A novel approach is proposed in this article to boost the energy efficiency (EE) of an AoI-aware IoT network. In particular, we propose a new approach that is based a combination of simultaneous wireless information and power
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500 Views
These datasets contain the database of the local television channels in the greater manila area (GMA) in the Philippines as of 2020. There are 8 databases corresponding to 8 eight channels namely Channel4.xlsx, Channel5.xlsx, Channel7.xlsx, Channel9.xlsx, Channel11.xlsx,
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Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions.
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133 Views
the measurement data simulated data of Hd-TCP and its comparisons' performance on the real high-speed railways scenario
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488 Views
This dataset contains in-silico results of insulin treatment using a fully automated artificial pancreas algorithm based on reinforcement learning for FDA-approved virtual patients (C. D. Man et al., 2014) with type 1 diabetes (10 adults and 10 adolescents).
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1188 Views
Supplementary Material for IEEE-TII Transaction Article "Controller Design for Electrical Drives by Deep Reinforcement Learning - a Proof of Concept"
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501 Views