3 citations · 6 across the 6 of their papers we have counts for
6 papers
Effect of Monetary Reward on Users' Individual Strategies Using Co-Evolutionary Learning
Shintaro Ueki, Fujio Toriumi, Toshiharu Sugawara
Consumer generated media (CGM), such as social networking services rely on the voluntary activity of users to prosper, garnering the psychological rewards of feeling connected with…
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…
Distributed Planning with Asynchronous Execution with Local Navigation for Multi-agent Pickup and Delivery Problem
Yuki Miyashita, Tomoki Yamauchi, Toshiharu Sugawara
We propose a distributed planning method with asynchronous execution for multi-agent pickup and delivery (MAPD) problems for environments with occasional delays in agents' activiti…
Deadlock-Free Method for Multi-Agent Pickup and Delivery Problem Using Priority Inheritance with Temporary Priority
Yukita Fujitani, Tomoki Yamauchi, Yuki Miyashita +1
This paper proposes a control method for the multi-agent pickup and delivery problem (MAPD problem) by extending the priority inheritance with backtracking (PIBT) method to make it…
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…
Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks
Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara
The multi-agent pickup and delivery (MAPD) problem, in which multiple agents iteratively carry materials without collisions, has received significant attention. However, many conve…