no code implementations • 3 Mar 2023 • Jihwan Oh, Joonkee Kim, Minchan Jeong, Se-Young Yun
In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.
Distributional Reinforcement Learning Multi-agent Reinforcement Learning +2
1 code implementation • 5 Jul 2022 • Mingyu Kim, Jihwan Oh, Yongsik Lee, Joonkee Kim, SeongHwan Kim, Song Chong, Se-Young Yun
This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.
Ranked #1 on SMAC+ on Off_Superhard_parallel
no code implementations • 28 Jun 2022 • Jihwan Oh, Joonkee Kim, Se-Young Yun
Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be used to explore.
Distributional Reinforcement Learning reinforcement-learning +2