関節付き動物AI：四肢エージェントにおける動物認知のための環境
Articulated Animal AI: An Environment for Animal-like Cognition in a Limbed Agent
動物の認知を評価するための既存環境を拡張し、エージェントに四肢を追加して複雑な行動や環境との相互作用を可能にした学習・評価ツールを提供する。
著者: Jeremy Lucas, Isabeau Prémont-Schwarz
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
This paper presents the Articulated Animal AI Environment for Animal Cognition, an enhanced version of the previous AnimalAI Environment. Key improvements include the addition of agent limbs, enabling more complex behaviors and interactions with the environment that closely resemble real animal movements. The testbench features an integrated curriculum training sequence and evaluation tools, eliminating the need for users to develop their own training programs. Additionally, the tests and training procedures are randomized, which will improve the agent's generalization capabilities. These advancements significantly expand upon the original AnimalAI framework and will be used to evaluate agents on various aspects of animal cognition.