The objective of a reinforcement learning agent is to discover the best actions through exploration. However, standard methods such as \(\epsilon\) -greedy and intrinsic motivation may rely excessively on random selection or the pursuit of novelty. As a result, they can drive exploration toward states unrelated to the actual learning objective, potentially leading to inefficient learning and delayed convergence. We propose a novel deep reinforcement learning method that prioritizes achieving a target return over maximizing return. This method directly utilizes the target return as a guiding signal for exploration, prioritizing actions that are more likely to achieve the target return. In addition, when the target has not yet been achieved, our agent actively explores the less frequently selected actions to discover better ones. Through experiments on a motion control task and a navigation task, our method demonstrated more stable and robust performance than standard methods, achieving higher returns with fewer episodes. These findings suggest that exploration based on target return can be an effective approach for practical applications whose priority is achieving a specific performance level under limited resources.
