The needs describe the necessities for a system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior. Based on Maslow hierarchy of needs, an agent needs to satisfy a certain amount of needs at the current level as a condition to arise at the next stage -- upgrade and evolution. Especially, Deep Reinforcement Learning (DAL) can help AI agents (like robots) organize and optimize their behaviors and strategies to develop diverse Strategies based on their current state and needs (expected utilities or rewards). This paper introduces the new hierarchical needs-driven Learning systems based on DAL and investigates the implementation in the single-robot with a novel approach termed Bayesian Soft Actor-Critic (BSAC). Then, we extend this topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.

翻译：需求描述了系统生存和进化所必需的条件，它激发智能体为达到目标而行动，为行为赋予目的和方向。基于马斯洛需求层次理论，智能体需要满足当前层次的特定需求，才能进入下一阶段——升级与进化。特别是，深度强化学习（DAL）能够帮助人工智能智能体（如机器人）根据其当前状态和需求（期望效用或奖励），组织和优化其行为与策略，以发展多样化的策略。本文介绍了基于DAL的新型层次化需求驱动学习系统，并探讨了其在单机器人场景中的实现，提出了一种称为贝叶斯软演员-评论家（BSAC）的新方法。随后，我们将该主题扩展到多智能体系统（MAS），讨论了潜在的研究领域与方向。