Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
This work addresses performance bottlenecks in quantum reinforcement learning for multi-agent systems, which is an incremental improvement over existing gradient-free methods.
The paper tackles the problem of barren plateaus in multi-agent quantum reinforcement learning by proposing three genetic variations with variational quantum circuits using evolutionary optimization. The results show that these approaches perform significantly better than neural networks with similar parameter counts and achieve comparable results to larger neural networks using 97.88% fewer parameters.
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.