With the networks becoming complex, traditional congestion control protocols face increasing challenges in providing high-quality services for users. Traditional TCP and its variants fail to achieve high performance due to drawbacks in architectural design: predefined actions to specific network feedback. In this paper, we develop a learning-based TCP congestion control scheme RLCC, featuring a deep Q-network framework, in which senders learn the optimal control policies from observations instead of predefined rules. To apply DQN algorithms to congestion control problems, we first prove theoretically that congestion control problems are of Markov property. Therefore, the model-free reinforcement learning algorithm DQN can be used to solve congestion control. This is because the application of DQN to the network congestion control problem is convergent, and there exists an optimal strategy to obtain the best action for congestion control. We improved the network's performance by carefully designing the reward function and choosing the appropriate form and parameters through extensive experimentation. Extensive experiments on real-world environments of Pantheon via AWS confirm that RLCC can achieve utilization improvements over the traditional TCP congestion control schemes with higher throughput and lower transmission latency, and outperform the recently proposed learning-based congestion control protocol.