Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors
<|reference_start|>Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors: Multi-agent reinforcement learning (MARL) has become a significant research topic due to its ability to facilitate learning in complex environments. In multi-agent tasks, the state-action value, commonly referred to as the Q-value, can vary among agents because of their individual rewards, resulting in a Q-vector. Determining an optimal policy is challenging, as it involves more than just maximizing a single Q-value. Various optimal policies, such as a Nash equilibrium, have been studied in this context. Algorithms like Nash Q-learning and Nash Actor-Critic have shown effectiveness in these scenarios. This paper extends this research by proposing a deep Q-networks (DQN) algorithm capable of learning various Q-vectors using Max, Nash, and Maximin strategies. The effectiveness of this approach is demonstrated in an environment where dual robotic arms collaborate to lift a pot.<|reference_end|>
@article{luo2024multi-agent,
    title={Multi-agent Reinforcement Learning with Deep Networks for Diverse
  Q-Vectors},
    author={Zhenglong Luo, Zhiyong Chen and James Welsh},
    journal={arXiv preprint arXiv:2406.07848},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07848},
    primaryClass={cs.AI cs.MA}
}