… which learns to optimize device placement for TensorFlow … placements is then used as the
reward signal to optimize the … model finds non-trivial device placements that outperform hand-…

… of a Reinforcement Learning approach to model a placement … we extend Neural Combinatorial
Optimization theory in order … network model is able to learn placement decisions, with the …

… onto a limited set of resources to optimize for an objective, … reinforcement learning as a
solution to the placement problem. We then give an overview of what deep reinforcement learning …

… be improved by using Reinforcement Learning (RL) to learn effective and adaptive heuristics.
Applying these techniques to Field Programmable Gate Array (FPGA) placement, we show …

… This paper proposes a deep reinforcement learning (RL) framework to optimize the placement
… Our RL algorithms are chosen to overcome the sparsity of data and latency of placement …

… This paper investigates the placement optimization of multiple ABSs to maximize the coverage
rate of GUs under the dominant-LoS channel model first, and further the site-specific LoS/…

… In this work, we target the chip placement optimization problem, in which the objective is to
map the nodes of a netlist (the graph describing the chip) onto a chip canvas (a bounded 2D …

… an initial analytical placement. While SA-… reinforcement learning (RL) and targeted
perturbations (directed moves). The proposed moves target both wirelength and timing optimization …

… server placement algorithm based on deep reinforcement learning, … Then, reinforcement
learning is applied to find the optimal … its placement decisions through a trial-and-error learning …

… a reinforcement-learning-based method to automatically optimize core placement through
… , placements) and using convolutional neural networks to extract spatial features of different …