Next, we evaluate MOLRL on multi-parameter optimization, and we perform a comparison with the current state-of-theart optimization methods. In particular, we apply MOLRL to generate molecules with biological activity for two targets and at the same time we optimize for drug likeness (QED) and synthetic accessibility (SA), following the benchmark adopted by Jin et al. . The selected biological targets are two kinases, GSK3β and JNK3, both associated with Alzheimer's disease. Binding affinity towards these targets is estimated using Machine Learning (ML) classification models developed by Li et al. and made available through the PyTDC software package .