4 citations · 17 across the 15 of their papers we have counts for
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cs.LG2023
Tempo Adaptation in Non-stationary Reinforcement Learning
Hyunin Lee, Yuhao Ding, Jongmin Lee +3
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-…
cs.LG2023★ 1 cited
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Donghao Ying, Yunkai Zhang, Yuhao Ding +2
We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraint…
cs.LG2023
Scalable Multi-Agent Reinforcement Learning with General Utilities
Donghao Ying, Yuhao Ding, Alec Koppel +1
We study the scalable multi-agent reinforcement learning (MARL) with general utilities, defined as nonlinear functions of the team's long-term state-action occupancy measure. The o…