Abstract

Reinforcement learning is a crucial technical achievement in the present era, playing a key role in generating substantial progress in artificial intelligence and machine learning applications across several areas. The application of this dynamic methodology empowers machines and software agents to independently acquire knowledge and enhance their performance through their interactions with their surroundings, hence improving problemsolving and decision-making activities. This study delves into the application of reinforcement learning techniques to train an autonomous agent within a customized Mountain Car climbing environment. The primary objective revolves around optimizing fuel usage to propel the car uphill, strategically balancing the need for ascent against the subsequent horizontal distance covered during the descent. The constrained resource, finite fuel, necessitates the agent's adaptation through a training process to discern an optimal fuel consumption strategy. The research underscores the iterative nature of this training process, wherein the agent learns to navigate the trade-off between fuel utilization and distance traveled. In essence, this investigation contributes to the realm of reinforcement learning by elucidating the agent's capacity to make informed decisions.