MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
Summary: arXiv:2604.11131v1 Announce Type: new
Abstract
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated by the cognitive methods used by humans, RL has become a widely accepted strategy in the field of artificial intelligence. However, most environments used for RL are often high-dimensional, making traditional RL algorithms computationally expensive and challenging to effectively learn from such systems.
Recent advancements in the practical demonstration of quantum computing (QC) theories, including compact encoding, enhanced representation and learning algorithms, random sampling, and the inherent stochastic nature of quantum systems, have opened up new directions to tackle these challenges. Quantum reinforcement learning (QRL) has gained significant traction over the past few years. Nonetheless, the current state of quantum hardware is not sufficient to cater to high-dimensional environments with complex multi-agent setups.
Proposed Framework
In response to these issues, we propose a distributed framework for Quantum Reinforcement Learning (MADQRL). This framework allows multiple agents to learn independently, effectively distributing the load of joint training across individual machines. Our method is particularly effective for environments with disjoint sets of action and observation spaces, and it can also be extended to other systems with reasonable approximations.
Methodology
The MADQRL framework utilizes a multi-agent approach where each agent operates in parallel, ensuring that the computational burden is shared. This not only enhances learning efficiency but also accelerates the convergence of policies. The key components of our methodology include:
- Independent Learning: Each agent learns from its own experience while still contributing to a shared objective.
- Load Distribution: The training load is distributed across multiple machines, allowing for scalable learning.
- Action and Observation Spaces: The framework is designed to handle environments with disjoint sets of actions and observations, facilitating better performance.
Experimental Results
We conducted extensive experiments to validate the effectiveness of the MADQRL framework. Our analysis was performed in a cooperative-pong environment, where we compared our approach against other distribution strategies and classical models of policy representation. The results were promising:
- Approximately 10% improvement in performance compared to other distribution strategies.
- About 5% improvement over classical models of policy representation.
Conclusion
The MADQRL framework represents a significant advancement in the field of quantum reinforcement learning, addressing the challenges posed by high-dimensional multi-agent environments. By leveraging the power of distributed learning, our approach not only improves efficiency but also enhances the overall learning process. As quantum hardware continues to evolve, we anticipate that the capabilities of MADQRL will expand, paving the way for more complex and capable AI systems in the near future.