1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.MA2026
Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents
Yamato Takahagi, Gentoku Nakasone, Yoshinari Motokawa +1
This study proposes a learning method for multi-agent systems that allows agents to be controlled through human manager instructions after learning and enables uninstructed agents…
cs.LG2023
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning
Yoshinari Motokawa, Toshiharu Sugawara
We propose a model-free reinforcement learning architecture, called distributed attentional actor architecture after conditional attention (DA6-X), to provide better interpretabili…
cs.AI2022★ 1 cited
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise
Yoshinari Motokawa, Toshiharu Sugawara
In multi-agent systems, noise reduction techniques are important for improving the overall system reliability as agents are required to rely on limited environmental information to…