Despite having achieved spectacular milestones in an array of important real-world applications, most Reinforcement Learning (RL) methods are very brittle concerning their hyperparameters. Notwithstanding the crucial importance of setting the hyperparameters in training state-of-the-art agents, the task of hyperparameter optimization (HPO) in RL is understudied. In this paper, we propose a novel gray-box Bayesian Optimization technique for HPO in RL, that enriches Gaussian Processes with reward curve estimations based on generalized logistic functions. We thus about the performance of learning algorithms, transferring information across configurations and about epochs of the learning algorithm. In a very large-scale experimental protocol, comprising 5 popular RL methods (DDPG, A2C, PPO, SAC, TD3), 22 environments (OpenAI Gym: Mujoco, Atari, Classic Control), and 7 HPO baselines, we demonstrate that our method significantly outperforms current HPO practices in RL.