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

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arxiv 2410.09275 v1 pith:M33BQKMW submitted 2024-10-11 cs.LG cs.AIcs.RO

classification cs.LGcs.AIcs.RO
keywords animalenvironmentagentcognitiontraininganimalaiarticulatedwill
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?

    cs.LG 2025-05 conditional novelty 6.0 of 10

    On two new curriculum benchmarks, direct-encoding neuroevolution (NEAT) transfers skills across levels better than PPO reinforcement learning, while indirect encodings like HyperNEAT transfer poorly.

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