3 citations · 4 across the 3 of their papers we have counts for
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cs.LG2022
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Furui Liu +1
Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of mult…
cs.LG2021★ 3 cited
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Jinxin Liu, Hao Shen, Donglin Wang +2
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals an…
cs.LG2021★ 1 cited
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
Jinxin Liu, Donglin Wang, Qiangxing Tian +1
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions. Considering such p…