Cloud computing has revolutionized the way organizations manage and utilize computational resources. However, the dynamic nature of cloud environments presents challenges in efficient resource allocation and management. This paper investigates the application of reinforcement learning (RL) algorithms for autonomous resource management in cloud computing environments. We explore various RL techniques, including Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO), and evaluate their effectiveness in optimizing resource allocation, load balancing, and energy efficiency. Our findings demonstrate that RL-based approaches can significantly improve resource utilization and reduce operational costs compared to traditional heuristic methods. We also discuss the challenges and limitations of implementing RL in cloud environments and propose future research directions.