Search Results for author: Jihwan Oh

Found 3 papers, 1 papers with code

Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning

no code implementations3 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

The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions

1 code implementation5 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.


Risk Perspective Exploration in Distributional Reinforcement Learning

no code implementations28 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

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