Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913633230913536 |
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| author | Kölle, Michael Topp, Felix Phan, Thomy Altmann, Philipp Nüßlein, Jonas Linnhoff-Popien, Claudia |
| author_facet | Kölle, Michael Topp, Felix Phan, Thomy Altmann, Philipp Nüßlein, Jonas Linnhoff-Popien, Claudia |
| contents | 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. While gradient free Quantum Reinforcement Learning methods may alleviate some of these challenges, they too are not immune to the difficulties posed by barren plateaus. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_05546 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization Kölle, Michael Topp, Felix Phan, Thomy Altmann, Philipp Nüßlein, Jonas Linnhoff-Popien, Claudia Quantum Physics Artificial Intelligence Multiagent Systems 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. While gradient free Quantum Reinforcement Learning methods may alleviate some of these challenges, they too are not immune to the difficulties posed by barren plateaus. 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. |
| title | Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization |
| topic | Quantum Physics Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2311.05546 |