We introduce a dynamic self-efficacy learning rule and examine its impact on multi-goal selection in a grid-world. We model the Q-learning agent's self-efficacy as the integral of reward prediction errors (RPEs), allowing it to modulate the agent's expectation of achieving the best possible future outcome. Initial simulation results suggest that faster self-efficacy updates lead to higher overall reward accumulation, but with increased variability in reaching the optimal goal. These findings indicate that an optimal self-efficacy update rate, which can be learned through experience, may strike a balance between maximizing performance and maintaining stability.