Multi-Agent Quantum Reinforcement Learning using Evolutionary
Optimization
- URL: http://arxiv.org/abs/2311.05546v2
- Date: Sat, 13 Jan 2024 10:59:54 GMT
- Title: Multi-Agent Quantum Reinforcement Learning using Evolutionary
Optimization
- Authors: Michael K\"olle, Felix Topp, Thomy Phan, Philipp Altmann, Jonas
N\"u{\ss}lein, Claudia Linnhoff-Popien
- Abstract summary: We build upon an existing approach for gradient free Quantum Reinforcement Learning and propose three genetic variations with Variational Quantum Circuits for Multi-Agent Reinforcement Learning.
We show that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
- Score: 7.305065320738301
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-Agent Reinforcement Learning is becoming increasingly more important in
times of autonomous driving and other smart industrial applications.
Simultaneously a promising new approach to Reinforcement Learning arises using
the inherent properties of quantum mechanics, reducing the trainable parameters
of a model significantly. However, gradient-based Multi-Agent Quantum
Reinforcement Learning methods often have to struggle with barren plateaus,
holding them back from matching the performance of classical approaches. We
build upon an existing approach for gradient free Quantum Reinforcement
Learning and propose three genetic variations with Variational Quantum Circuits
for Multi-Agent Reinforcement Learning using evolutionary optimization. We
evaluate our genetic variations in the Coin Game environment and also compare
them to classical approaches. We showed that our Variational Quantum Circuit
approaches perform significantly better compared to a neural network with a
similar amount of trainable parameters. Compared to the larger neural network,
our approaches archive similar results using $97.88\%$ less parameters.
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