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In this paper, we propose textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show releta can reduce the average peak temperature by 4 $^{circ}$C and the maximum difference is up to 13 $^{circ}$C.
The effective allocation of cross-border trading capacities is one of the central challenges in implementation of a pan-European internal energy market. Flow-based market coupling has shown promising results for to achieve better price convergence be
Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era a
In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, w
Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks. While previous efforts attempt to address this challenge using meta-learning strategies, they typi
This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate