  Offline reinforcement learning (RL) extends the paradigm of classical RL
algorithms to purely learning from static datasets, without interacting with
the underlying environment during the learning process. A key challenge of
offline RL is the instability of policy training, caused by the mismatch
between the distribution of the offline data and the undiscounted stationary
state-action distribution of the learned policy. To avoid the detrimental
impact of distribution mismatch, we regularize the undiscounted stationary
distribution of the current policy towards the offline data during the policy
optimization process. Further, we train a dynamics model to both implement this
regularization and better estimate the stationary distribution of the current
policy, reducing the error induced by distribution mismatch. On a wide range of
continuous-control offline RL datasets, our method indicates competitive
performance, which validates our algorithm. The code is publicly available.
