Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies
This work addresses the challenge of adaptive behavior in AI agents, such as robots, by integrating psychological needs theory, but it appears incremental as it builds on existing reinforcement learning frameworks.
The paper tackles the problem of enabling AI agents to develop diverse strategies by introducing a hierarchical needs-driven learning system based on Deep Reinforcement Learning, with results including a novel Bayesian Soft Actor-Critic method for single-robot implementation and extension to multi-agent systems.
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.