Our key insight is that the user's examination and click behavior

We can compute the fixed point of the recursion in Equation A.2 and get the following estimated Then we compare these two gaps. To utilize the Eq. 4 for policy optimization, following the analysis in the Section 3.2 in Kumar et al. By choosing different regularizer, there are a variety of instances within CQL family. B.36 called CFCQL( H) which is the update rule we used: In discrete action space, we train a three-level MLP network with MLE loss. In continuous action space, we use the method of explicit estimation of behavior density in Wu et al.

MARL in real scenarios is still challenging due to the same safety and efficiency concerns in single-agent setting, then it is worth conducting investigation for offline RL in multi-agent setting.

However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].