  We consider the offline reinforcement learning (RL) setting where the agent
aims to optimize the policy solely from the data without further environment
interactions. In offline RL, the distributional shift becomes the primary
source of difficulty, which arises from the deviation of the target policy
being optimized from the behavior policy used for data collection. This
typically causes overestimation of action values, which poses severe problems
for model-free algorithms that use bootstrapping. To mitigate the problem,
prior offline RL algorithms often used sophisticated techniques that encourage
underestimation of action values, which introduces an additional set of
hyperparameters that need to be tuned properly. In this paper, we present an
offline RL algorithm that prevents overestimation in a more principled way. Our
algorithm, OptiDICE, directly estimates the stationary distribution corrections
of the optimal policy and does not rely on policy-gradients, unlike previous
offline RL algorithms. Using an extensive set of benchmark datasets for offline
RL, we show that OptiDICE performs competitively with the state-of-the-art
methods.
