Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Takumi Aotani"'
Publikováno v:
IEEE Access, Vol 9, Pp 148783-148799 (2021)
Model-based reinforcement learning is expected to be a method that can safely acquire the optimal policy under real-world conditions by using a stochastic dynamics model for planning. Since the stochastic dynamics model of the real world is generally
Externí odkaz:
https://doaj.org/article/af14030d799748c8865f08da2aa6ba56
Autor:
Taisuke Kobayashi, Takumi Aotani
Publikováno v:
Advanced Robotics. :1-18
Publikováno v:
Applied Intelligence. 51:4434-4452
A multi-agent system (MAS) is expected to be applied to various real-world problems where a single agent cannot accomplish given tasks. Due to the inherent complexity in the real-world MAS, however, manual design of group behaviors of agents is intra
Autor:
Emmanuel Dean-Leon, Gordon Cheng, Julio Rogelio Guadarrama-Olvera, Takumi Aotani, Taisuke Kobayashi
Publikováno v:
ICDL-EPIROB
This paper presents a new actor-critic (AC) algorithm based on a biological reward-punishment framework for reinforcement learning (RL), named “RP-AC”. RL can yields capabilities where robots can take over complicated and dangerous tasks instead
Publikováno v:
SMC
Applications of multi-agent system like cooperative transport are found in various domains of real world. Due to the complexity inherent in multi-agent system, however, handling with preprogramming is difficult. Multi-agent reinforcement learning (MA
Publikováno v:
The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2018:1P1-E17