Sep 22, 2021 · This novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) for Q-value approximation has outperformed regular deep reinforcement learning.
This chapter describes the basics needed to under- stand our proposed architecture. First, reinforcement learning and the underlying Markov Decision Process.
Towards Multi-agent Reinforcement Learning using Quantum Boltzmann Machines. Müller, T., Roch, C., Schmid, K., & Altmann, P. 2022.
This work designs a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the ...
We show that the use of quantum annealing can improve the learning compared to classical methods. We do this both by means of actual quantum hardware and by ...
Nov 22, 2021 · This novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) for Q-value approximation has out- performed regular deep reinforcement ...
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines. T Müller, C Roch, K Schmid, P Altmann. International Conference on Agents and ...
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It is shown that the use of quantum annealing can improve the learning compared to classical methods, and is extended based on quantum Boltzmann machines, ...
We show that the use of quantum annealing can improve the learning compared to classical methods. We do this both by means of actual quantum hardware and by ...
Oct 22, 2024 · We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks.