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One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
Bhavith Chandra Challagundla, Sanskar Pandey, Param Thakkar +7
World models are now built on substantially different computational substrates. Latent recurrent state-space models such as PlaNet and the Dreamer family compress observations into…
Mental Modeling of Reinforcement Learning Agents by Language Models
Wenhao Lu, Xufeng Zhao, Josua Spisak +2
Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically ca…
Causal State Distillation for Explainable Reinforcement Learning
Wenhao Lu, Xufeng Zhao, Thilo Fryen +4
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack…