In the field of algorithm optimisation, finding the optimal configuration of parameters is essential for achieving peak performance. Traditional methods usually rely on manual tuning or exhaustive search techniques, which can be time-consuming and inefficient. This paper proposes a novel approach utilising deep reinforcement learning (DRL) for automatically tuning the parameters of those algorithms that require it. By formulating the parameter tuning problem of the Holt-Winters algorithm as a reinforcement learning task, this research provides an example of the use of this method, which enables algorithms to autonomously tune their parameters based on feedback from the environment shaped by the problem. Leveraging an advantage actor-critic algorithm (A2C), our framework learns optimal parameter settings through exploration and exploitation strategies. The main findings highlight the versatility and efficiency of this DRL-based method for parameter tuning in optimising algorithms, paving the way for more adaptive and self-improving systems in various domains.