  Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which
learns the value function using only dataset actions through quantile
regression. However, it is unclear how to recover the implicit policy from the
learned implicit Q-function and why IQL can utilize weighted regression for
policy extraction. IDQL reinterprets IQL as an actor-critic method and gets
weights of implicit policy, however, this weight only holds for the optimal
value function. In this work, we introduce a different way to solve the
implicit policy-finding problem (IPF) by formulating this problem as an
optimization problem. Based on this optimization problem, we further propose
two practical algorithms AlignIQL and AlignIQL-hard, which inherit the
advantages of decoupling actor from critic in IQL and provide insights into why
IQL can use weighted regression for policy extraction. Compared with IQL and
IDQL, we find our method keeps the simplicity of IQL and solves the implicit
policy-finding problem. Experimental results on D4RL datasets show that our
method achieves competitive or superior results compared with other SOTA
offline RL methods. Especially in complex sparse reward tasks like Antmaze and
Adroit, our method outperforms IQL and IDQL by a significant margin.
