MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for RL are often high-dimensional, and traditional RL algorithms becomes computationally expensive and challenging to effectively learn from such systems. Recent advancements in practical demonstration of quantum computing (QC) theories, such as compact encoding, enhanced representation and learning algorithms, random sampling, or the inherent stochastic nature of quantum systems, have opened up new directions to tackle these challenge...
| Authors | Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli et al. |
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| Primary category | cs.AI |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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