認知移動ロボットにおける動機づけモデルによる目標ベース移動の学習
Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots
ハルの動機理論を計算モデル化し、快楽次元と強化学習を組み合わせて、移動ロボットが代謝に応じて生存と快を両立する行動を学習する手法を提案した。
著者: Letícia Berto, Paula Costa, Alexandre Simões, Ricardo Gudwin, Esther Colombini
分類: cs.RO, cs.LG
原文アブストラクト
Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios.