  Robot navigation has transitioned from prioritizing obstacle avoidance to
adopting socially aware navigation strategies that accommodate human presence.
As a result, the recognition of socially aware navigation within dynamic
human-centric environments has gained prominence in the field of robotics.
Although reinforcement learning technique has fostered the advancement of
socially aware navigation, defining appropriate reward functions, especially in
congested environments, has posed a significant challenge. These rewards,
crucial in guiding robot actions, demand intricate human-crafted design due to
their complex nature and inability to be automatically set. The multitude of
manually designed rewards poses issues with hyperparameter redundancy,
imbalance, and inadequate representation of unique object characteristics. To
address these challenges, we introduce a transformable gaussian reward function
(TGRF). The TGRF significantly reduces the burden of hyperparameter tuning,
displays adaptability across various reward functions, and demonstrates
accelerated learning rates, particularly excelling in crowded environments
utilizing deep reinforcement learning (DRL). We introduce and validate TGRF
through sections highlighting its conceptual background, characteristics,
experiments, and real-world application, paving the way for a more effective
and adaptable approach in robotics.The complete source code is available on
https://github.com/JinnnK/TGRF
