This paper investigates the efficacy of various reinforcement learning (RL) algorithms for decision-making within the domain of algorithmic trading. We propose a comprehensive methodology encompassing the development, implementation, and evaluation of RL-driven trading models using historical market data. The study incorporates five prominent RL approaches: Actor-Critic, Moving Average, Policy Gradient, Q-Learning, and Double Duel Q-Learning. To assess their performance, we employ established metrics such as total gains, Return on Investment (ROI), and winning trades percentage. Our analysis reveals that Policy Gradient and Double Duel Q-Learning strategies achieve demonstrably superior overall performance. This finding suggests their particular suitability for navigating the inherent complexities of stock market dynamics. Conversely, the Moving Average model, characterized by its relative simplicity, yields the weakest results, highlighting the advantages of more intricate RL techniques. These insights offer valuable guidance for researchers and practitioners seeking to leverage advanced RL methods for the optimization of algorithmic trading systems.