no code implementations • 3 Jul 2019 • Akira Kinose, Tadahiro Taniguchi
In this paper, we present a new theory for integrating reinforcement and imitation learning by extending the probabilistic generative model framework for reinforcement learning, {\it plan by inference}.
no code implementations • 15 Mar 2022 • Akira Kinose, Masashi Okada, Ryo Okumura, Tadahiro Taniguchi
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming.
no code implementations • 6 Feb 2024 • Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, Yang You
Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming, and API integration.