  Learning agents can make use of Reinforcement Learning (RL) to decide their
actions by using a reward function. However, the learning process is greatly
influenced by the elect of values of the hyperparameters used in the learning
algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and
Hindsight Experience Replay (HER) based method, which makes use of the Genetic
Algorithm (GA) to fine-tune the hyperparameters' values. This method
(GA+DDPG+HER) experimented on six robotic manipulation tasks: FetchReach;
FetchSlide; FetchPush; FetchPickAndPlace; DoorOpening; and AuboReach. Analysis
of these results demonstrated a significant increase in performance and a
decrease in learning time. Also, we compare and provide evidence that
GA+DDPG+HER is better than the existing methods.
