We have introduced MOLRL, a novel approach for targeted molecule generation combining powerful generative models and Reinforcement Learning. Molecular optimization takes place in the latent space of an autoencoder generative model trained on molecules. The latent space provides a continuous representation of molecules suitable for facilitating optimization tasks and bypassing the need for explicitly accounting for chemical validity through pre-defined chemical rules or heuristics. MOLRL shows superior or competitive performance across a variety of tasks for targeted molecular generation and multi-parameter optimization when compared with established methods.