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

Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies