Articulated Animal AI: An Environment for Animal-like Cognition in a Limbed Agent

Jeremy Lucas, Isabeau Prémont-Schwarz

Keywords: Reinforcement Learning, Animal Cognition, Agent Learning, Open World, Environment, Skill reuse

TL;DR: An Environment to train and test animal-like cognition, but with a limbed agent which also need to learn to walk.

Abstract: 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.

Submission Number: 66