Herein we introduce MOLRL (Molecule Optimization with Latent Reinforcement Learning), a novel framework for molecule optimization leveraging powerful pre-trained latent space generative models and RL. MOLRL performs optimization in the latent space of a pre-trained generative model using Proximal Policy Optimization (PPO) , a state-of-the-art policy gradient RL algorithm. PPO can achieve a good trade-off between exploration and exploitation while also maintaining a trust region which is critical for searching challenging environments such as chemical latent spaces. The proposed framework is agnostic to the architecture of the generative model; however, the characteristics of the latent space can have a significant impact on the performance of the optimization method.