The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
원문 초록
The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI Testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Reinforcement Learning with Gradient Eligibility Traces
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
토론
이 논문과 연결된 포럼 글이 아직 없습니다.