  We present 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.
