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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Yoshinari Motokawa

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Conference Open access Nov 2025

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents

The proposed method, which extends previous work on controllability in multi-agent deep reinforcement learning, enables uninstructed agents to adaptively complement overlooked tasks and areas and enables uninstructed agents to implicitly complement the overall work based on the actions of other agents.

Y. Takahagi, Gentoku Nakasone, Yoshinari Motokawa et al. · 0 citations
Conference Jul 2026

Language-Grounded Strategy-Following Multi-Agent Deep Reinforcement Learning for Controllability of Real-World Applications

We present lg-sfDA6-X, a language-grounded strategy-following distributed attentional actor architecture after conditional attention, for multi-agent deep reinforcement learning (MADRL). The proposed architecture aims to enable controllable and coordinated agent behaviors in application systems by leveraging a shared s...

Yoshinari Motokawa, Toshiharu Sugawara · 0 citations

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