自律エージェントにおけるエナクティブ学習の検討
Investigating Enactive Learning for Autonomous Intelligent Agents
迷路内の採餌タスクで、環境との直接的な感覚運動相互作用に基づくエナクティブ学習エージェントと従来の強化学習エージェントの性能を比較し、エナクティブエージェントが内在的目標により不利な相互作用を回避して学習できる一方、行動数に性能が制限されることを示した。
著者: Rafik Hadfi
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.