MOLRL consists of two major components: a latent space generative model, and a RL agent. The generative model is a pre-trained encoder-decoder model whose latent space encodes the chemical space where the RL agent operates in. The RL agent is being trained to navigate the latent space using PPO, a policy gradient RL algorithm. The reward function provides feedback to the agent for learning how to navigate the space to identify molecules with desired properties.