5 papers
Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning
Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi
In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representation learning-based approaches…
EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation
Haruumi Omoto, Tadahiro Taniguchi
Measuring the extent to which emergent languages encode the visual content of their inputs is an open problem. We refer to this property as visual reflection: the extent to which e…
Emergent Communication between Heterogeneous Visual Agents through Decentralized Learning
Mikako Ochiai, Masatoshi Nagano, Tadahiro Taniguchi
Symbols are shared, but perception is private. We study emergent communication between heterogeneous visual agents through decentralized learning, asking what visual information ca…
Beyond Individuals: Collective Predictive Coding for Memory, Attention, and the Emergence of Language
Tadahiro Taniguchi
This commentary extends the discussion by Parr et al. on memory and attention beyond individual cognitive systems. From the perspective of the Collective Predictive Coding (CPC) hy…
Generative Emergent Communication: Large Language Model is a Collective World Model
Tadahiro Taniguchi, Ryo Ueda, Tomoaki Nakamura +2
Large Language Models (LLMs) have demonstrated a remarkable ability to capture extensive world knowledge, yet how this is achieved without direct sensorimotor experience remains a…