Derin takviyeli öğrenme ile atarı oyunlarını güçlendirmek

Deep Reinforcement Learning (DRL) has made significant strides in the domain of gaming, optimizing artificial intelligence agents to navigate intricate game environments. This study embarks on an exploration of the Snake Optimization Algorithm (SOA) and the Energy Valley Optimization (EVO), it synergizes into a unified approach, aptly named the Energy Serpent Optimizer (ESO), benchmarking their efficacy within a maze-like game setting. Within this environment, an AI agent is set to maneuver through complex pathways, while engaging with diverse challenges such as snakes, skulls, and other animated characters. The overarching objective for the agent is to adeptly navigate the maze, sidestep potential threats, and engage with specific game elements to amass points. A comparative analysis of ESO revealed notable differences in their execution time efficiencies. The SOA emerged as the more time-efficient algorithm, clocking in at a mere 0.43 seconds, This discernible time gap accentuates the superior efficiency of ESO in this particular setting. Additionally, the ESO was put to the test in the said game environment, where the optimization process entailed evolving a set of hyperparameter configurations via genetic algorithms. The goal was to iterate and adapt these configurations to maximize the AI agent's in-game reward, while ensuring the process is time-efficient. Impressively, the AI agent, under the guidance of ESO, achieved a remarkable reward score of 1100.0 in a span of 32 seconds.