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State Advantage Weighting for Offline RL
Jiafei Lyu · aicheng Gong · Le Wan · Zongqing Lu · Xiu Li

@
in 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Event URL: https://openreview.net/forum?id=2rOD_UQfvl »
We present \textit{state advantage weighting} for offline reinforcement learning (RL). In contrast to action advantage $A(s,a)$ that we commonly adopt in QSA learning, we leverage state advantage $A(s,s^\prime)$ and QSS learning for offline RL, hence decoupling the action from values. We expect the agent can get to the high-reward state and the action is determined by how the agent can get to that corresponding state. Experiments on D4RL datasets show that our proposed method can achieve remarkable performance against the common baselines. Furthermore, our method shows good generalization capability when transferring from offline to online.

Author Information

Jiafei Lyu (Tsinghua University, Tsinghua University)
aicheng Gong (Electronic Engineering, Tsinghua University, Tsinghua University)
Le Wan (Jilin University)
Zongqing Lu (Peking University)
Xiu Li

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