Setting Reward Function of Sensor Based DDQN Model
Mehmet Gökçay KABATAŞ, Sevinç İlhan Omurca · European Journal of Science and Technology · 2021
In this study, it is aimed to determine the appropriate reward function of the agent which trained to pass 100 obstacles/objects in Reinforcement Learning (RL) with Double Deep Q Network (DDQN) model. To train the agent, environment is split into sub problems. Several rules and different reward functions defined for the sub problems. A developed mini deep learning library which is called gNet is used for the training.