2 papers
cs.AI2026
GAI: Generative Agents for Innovation
Masahiro Sato
This study examines whether collective reasoning among generative agents can facilitate novel and coherent thinking that leads to innovation. To achieve this, it proposes GAI, a ne…
cs.LG2024
Generalized Back-Stepping Experience Replay in Sparse-Reward Environments
Guwen Lyu, Masahiro Sato
Back-stepping experience replay (BER) is a reinforcement learning technique that can accelerate learning efficiency in reversible environments. BER trains an agent with generated b…